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CHEMICAL ENGINEERING METHODS AND TECHNOLOGY

C

ONTINUOUS

P

ROCESS

D

YNAMICS

,

S

TABILITY

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C

ONTROL

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UTOMATION

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

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C

HEMICAL

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NGINEERING

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ETHODS

AND

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ECHNOLOGY

Additional books in this series can be found on Nova‟s website under the Series tab.

Additional e-books in this series can be found on Nova‟s website under the e-book tab.

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CHEMICAL ENGINEERING METHODS AND TECHNOLOGY

C

ONTINUOUS

P

ROCESS

D

YNAMICS

,

S

TABILITY

,

C

ONTROL

AND

A

UTOMATION

K

AL

R

ENGANATHAN

S

HARMA

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Copyright © 2015 by Nova Science Publishers, Inc.

All rights reserved. No part of this book may be reproduced, stored in a retrieval system or

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NOTICE TO THE READER

The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‟ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication.

This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

Additional color graphics may be available in the e-book version of this book.

LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

Continuous process dynamics, stability, control and automation / editors, Kal Renganathan Sharma. pages cm. -- (Chemical engineering methods and technology)

Includes index.

1. Chemical process control. I. Sharma, Kal Renganathan. TP155.75.C665 2014

660'.2815--dc23 2014044403

Published by Nova Science Publishers, Inc.

New York

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To my eldest son, R. Hari Subrahmanyan Sharma (alias Ramkishan)

who turned thirteen on August 13

th

2014

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C

ONTENTS

Preface ix

About the Author xvii

Book Description xix

Chapter 1 Introduction 1

Chapter 2 Continuous Polymerization Process Technology 33

Chapter 3 Mathematical Process Models 59

Chapter 4 Continuous Process Dynamics 249

Chapter 5 Proportional, Proportional Integral, Proportional Derivative

Feedback Control 323

Chapter 6 Frequency Response Analysis 369

Chapter 7 Advanced Control Methods 393

Chapter 8 Pharmacokinetic Analysis 487

Chapter 9 Instruments 527

Chapter 10 Nanorobots in Nanomedicine 545

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P

REFACE

Automation came about after mechanization. Chemical process control is a subset of the field of automation theory and practice. AIChE, American Institute of Chemical Engineers, New York, NY recently celebrated their centennial or 100th anniversary in 2008. Both the CPI and the industrial controls market size is increasing. Process Control as a required course in the chemical engineering degree plan in a number of US universities came out in the late 60s or 70s. I instruct CHEG 4033 Process Dynamics and Control, MCEG 3073 Automatic Controls and MCEG 3193 Introduction to Robotics, CHEG 3043 Equilibrium Staged Separations, ELMT 2043 Industrial Electronics etc. I have served as the chairman of Continuous Mass Club: Technical Community of Monstanto Minichapter between 1990-1993. This book is a natural outgrowth of these scholarly and research activities. A modern textbook is proposed that keeps pace with the progress in personal computing resources available to the student these days. Emphasis is placed on industrial application over rosy predictions. For instance, ideal PID control, proportional integral derivative control is not physically realizable. The competition of this book has a entire chapter devoted to PID control. Filters have to be added and then it is longer PID, it becomes FPID, filtered PID control! Discussions on overshoot is in excess of its occurrence in real applications. A careful scrutiny of overshoot can lead to the conclusion that it is more a mathematical artifact and less a engineering possibility It can be shown that overshoot per se violates second law of thermodynamics.. This book is more focused on the applications rather than on tuning and elloborate algebra. Calculus, Laplace transforms and other mathematical methods are used when necessary. A entire chapter is devoted to pharmacokinetic studies. Discussions on life systems draws the student attention more than hypothetical mathematical exercises. A entire chapter is devoted to control of robots. Advances made in nanorobots are also discussed in a separate chapter. Separate Chapters are devoted to mathematical models and process dynamics.

Lessons in control are drawn from Trommsdorf effect, thermal wear design, 2 arm manipulator, Fukushima earthquake and deepwater horizon oil spill.

Mathematical models can be used where pilot plant data is not available for scale-up. trouble shooting and in globalization. Process models can be classified as: A. Simulations from the Computer; B. Semi-Empirical Models; C. Mechanistic Models; D. Shell Balance Models; E. State Space Models; F. Dimensionless groups; G. Stochastic Models; H. Thermodynamic Analysis; I. Optimization Studies; J. Engineering Analysis and; K. Molecular Basis for Consitutive Laws. State space models can be used to describe a set of

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variables in vector form in terms of matrix equations. The stability of the system can be studied using Eigenvalues and Eigenvector analysis. A scheme of 7 simultaneous simple irreversible reactions were considered. The state space model to describe this system is given by Eq. (2.90). This system can be viewed as an integrating system since all but Eigenvalues are negative with 4 Eigenvalues 0 The composition of a copolymer as a function of comonomer composition, reactivity ratios and reactor choice were derived from the kinetics of free radical initiation, propagation and termination reactions. The copolymerization equation is obtained using the QSSA quasi-steady-state approximation. As an example, copolymer composition with 4 monomers as a function of monomer composition made in CSTR is illustrated 1 The copolymer composition is sensitive to reactivity ratios to a considerable extent. The copolymer composition equation for a multi-component copolymer with n monomers were derived. This methodology was generalized for n monomers. The general form of the equation was represented in the matrix form using linear algebra. The rate matrix and rate equation are given. The QSSA can be written in the vector notation. When the Eigen values of the rate equation become imaginary the monomer concentration can be expected to undergo subcritical damped oscillations as a function of time.

The occurrence of multiplicity in model solutions was illustrated by calculation of launch angle of stream of water during firefighting and elbow-up and elbow down solutions in the solution of inverse kinematics of a 3 arm manipulator with end effector (robot) and for a copolymer for certain values of reactivity ratios.

Use and significance of dimensionless groups such as Reynolds number, Prandtl number, Biot number, Nusselt number, Mach number, Fourier number, Fick number, Newton number, Maxwell number (mass), Venrnotte number, Sharma number (mass), Maxwell number (momentum), Sherwood number, Shcmidt number, storage number/Sharma number (heat), Peclect number were discussed. Weiner-Hopf integral equation can be used to estimate the effects of mixing in a CSTR, contiuous stirred tank reactor. This can be done by estimating the degree of mixedness from the variance in SPC, statistical process control charts. The compositional distribution of AN in copolymer can be used as input. J is minimized with respect to  and the Weiner-Hopf integral equation is obtained. Groot and Warren developed a mesoscopic simulation tool. Molecules are treated as spherical objects that can obey the Newton‟s laws of motion. Physical properties and EOS of polymeric substances were obtained from this analysis. Conservative, dissipative and random forces were taken into account. In the canonical ensemble, the Gibbs-Boltzmann distribution can seen to be the solution to the Fokker Planck equation.

The transient conversion in a PFR, plug flow reactor was derived. For a reaction of first order assuming that the conversion is analytic in space and time the governing equation can be shown to be of the hyperbolic type. For the zeroth order reaction the governing equation was found to be a wave equation! The hyperbolic PDEs can be solved for using the methods of relativistic transformation of coordinates leading to modified Bessel composite function solution, method of separation of variables and method of Laplace transforms. Numerical solution procedures can be used when the equations become nonlinear such as in the case of free radical polymerization reactions.

Transient analysis of Denbeigh scheme of reactions, first order reversible reactions, autocatalytic reactions, second order reversible reactions, reactions in series, Michaelis and Menten kinetics, reactions in circle kinetics are discussed in detail. The transient concentration of oxygen during diffusion and reaction in islets of Langerhaans are also

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Preface xi discussed in detail. The conditions where the concentration can undergo underdamped oscillations were derived. The use of final time condition and physically reasonable energy balance considerations can lead to solutions that are bounded and within the scope of the second law of thermodynamics. The temperature profile in a PFR is also obtained. Energy balance under transient conditions are used in the analysis

Transient dynamics of concentration, temperature separately and concentration and temperature both in a CSTR were discussed. The dimensionless group called Damkohler number in addition to conversion, dimensionless time, residence time were used to describe the transient output response from a CSTR. The hydrolysis reaction of ethylene oxide to ethylene glycol was considered in a CSTR. For incompressible flow and for a constant volume system the steady state and transient conversion were derived. The transient model solution is given. The conversion of ethylene oxide as a function of dimensionless time contoured at various values of Damkohler number are displayed CSTR with recycle was considered.

The transient temperature in a mixing tank that is heated is given. A fourth order Runge-Kutta method was used for numerical integration of the ODE, ordinary differential equation. Temperature vs. Time in reactor as response to a step change in input is obtained and displayed. A state space model was developed to describe a jacketed CSTR. The output variables of interest are dimensionless conversion, dimensionless temperature and is give. If all of the Eigenvalues of the state space A matrix are negative then the system is expected to be stable. A system with one Eigenvalue zero and one Eigenvalue negative is called an integrating system. If the Eigenvalues are complex conjugates the system is considerd oscillatory. Sometimes the oscillatory output response may be subcritical and damped, underdamped or critically damped. The stability type and characterization of stability for each of these cases are given in Table 4.3.

The transient concentration of initiator and monomer during free radical polymerization in a CSTR was obtained. DaI, Damkohler number (initiator) and DaM, Damkohler number

(monomer) were introduced. The steady state conversions were obtained as a function of the Damkohler number. Equation used to describe the transient conversion of monomer is non-linear. The conversion of initiator and monomer as a function of time is displayed in Figure 4.9. Multiplicity was found in model solution of conversion of monomer. Conversion of initiator was monotonic. It can be seen from Figure 4.9 for the parameters used in the simulation study the transient conversion of monomers undergoes a maximum value.

The general form of prototypical first order and prototypical second order system were provided. The first order process is characterized by process gain constant, kp and process

time constant, p. The second order process is characterized by the process gain, kp, damping

coefficient,  and process time constant, p. When the damping coefficient,  > 1 the system is

overdamped, when  = 1 the system is said to be critically damped and when  < 1 the system is expected to undergo a overshoot and is said to be underdamped oscillatory.

The use of on-off controller in CHP, combined heat and power system was discussed in detail. On-off controllers are used to shut down the system when output variable is greater than the desired limit. The system is turned back on when the output variable is lower than desired limits. Control action that is proportional to the error measured is called P only, proportional only controllers. Off-set is seen in P only controllers. PI, proportional integral

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control is devised to get rid of the offset. The control action is proportional to the integral of the error generated from initial time to a given instant in time.

In Example 5.1, the PI control action for prototypical first order process was discussed. The output function to a step change in input was obtained by inversion of Laplace transform expressions. A closed loop transfer function GCL(s) was derived for the combined system. The

conditions when the system will exhibit an overshoot are derived. In Example 5.2 the closed loop transfer function of a hybridized feed forward and feedback control loop was obtained. How the blocks in a control block diagram can be simplified was illustrated. In Example 5.3, PI control of voltage supplied to automatic washing machine was discussed. A Toshiba patent discussed such a machine. Although the model equation predicts the relation between the rotor speeds with the applied torque, there is no model available for applied voltage on the motor and the applied torque. The controller will have to increase the voltage or decrease the voltage, check the angular speed against set point sp and adjust the voltage again till that set

point angular speed is attained. PI controller was used. The closed loop transfer function GCL(s) was derived. Conditions for instability was when Gp(s)Gc(s) >> -1. The conditions for

underdamped oscillatory conditions to arise during PI control of prototypical first order process were when;

Where I is the time constant of PI controller, p is the time constant of prototypical first

order process, kc is the controller gain constant and kp is the process gain constant.

During PD control, proportional derivative control, the action taken is proportional to the derivative of the error between the measurement and set point.

Ideal PID control is not realizable due to the numerator dynamics. The order of the polynomial P(s) is greater than the order of the polynomial in the denominator Q(s) in the controller transfer function. As will be discussed later, a filter of nth order can be added to make the PID controller physically realizable.

The PI control of concentration during hydrolysis of ethylene oxide was discussed in Example 5.4. The output transfer function y(s) is given by Eq. (5.73). The conditions for underdamped response can be seen to be;

The output transfer function for a system with PD control of prototypical first order process is given by Eq. (5.79). The output function, y(t) is given by Eq. (5.84). For any value of the tie constants and gains of the process and controller the output is stable and has a monotonic exponential decay. In Example 5.5 the PD control of concentration during the formation of ethylene glycol in a CSTR by hydrolysis process was discussed. The conditions for underdamped response are given by Eq. (4.93).

PI control of concentration during formation of intermediate during reactions in series was discussed in Example 5.6. The stability of this system of PI control of intermediate product yield of chloroform, CHCl3 can be determined from the poles of Eq. (5.74). As the

order of the polynomial in the denominator is increased beyond quadratic, cubic closed form analytical solutions are not possible. Numerical solutions are needed to obtain a solution of the polynomial of higher degrees. Routh stability criteria can be used to analyze the stability of systems such as the one encountered here. Conditions for instability were derived and displayed.

Tyreus-Luyben oscillation based tuning was found to make the system less oscillatory for the parameters chosen. There are three important tuning parameters in PID control action. These are: (i) the control gain constant, kc; (ii) integral time constant, I; (iii) derivative time

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Preface xiii constant, D. An algorithmic or trial and error approach can be used for obtaining optimal

results from tuning the controller. The tuning of PID control was developed by Ziegler and Nichols [5]. This method is not widely used in the industry because the closed loop behavior tends to be oscillatory and sensitive to errors. The Tyreus-Luyben tuning parameters for PI and PID control are given in Table 5.1.

Closed loop stability analysis is discussed for a number of systems such as: (i) PI control of prototypical first order process; (ii) PI control of prototypical second order underdamped and overdamped processes; (iii) PI control of prototypical third order process; (iv) PI control of Integrating Stable Systems; (v) PI control of marginally stable systems; (vi) PI control of systems that make a center and/or saddle pointl (vii) Best control strategy for systems with inverse response; (viii) P, PD, PI control for jacketed exothermic CSTR; (ix) P, PD, PI control for exothermic PFR; (x) P control of polymerization kettle; (xi) P control of CSTR with recyle and PFR with recyle; (xii) Best Control Strategy for Systems with Dead Time; (xiii) Feedback control during semiconductor processing; (xiii) Systems with periodic disturbance; (xiv) Optimization and control of intermediate product in reactions in series with first first order and second zeroth order, first with zeroth order and second with first order, both first and second first order; (xv) PI control of intermediate species in Denbeigh scheme of reactions; (xvi) Best control strategy for PCR, polymerase chain reactions; (xvii) Best control strategy for systems that obey Michaelis and Menten kinetics; (xviii) Best control strategy for systems that obey reactions in circle kinetics.

The mathematical models, experimental trials and computer simulations that are undertaken to study the change of concentration of drug or other compounds of interest with time in the human physiology is called Pharmacokinetics. Application of pharmacokinetics allows for the processes of liberation, absorption, distribution, metabolism and excretion to be characterized mathematically. The absorption of drug can be affected by 14 different methods. The change with time of the concentration of the drug can be by three different types as shown in Figure 8.1: i) Slow absorption (A); ii) maxima and rapud bolus (B); iii) constant rate delivery (C). Pharmacokinetics studies can be performed by 5 different methods including compartment methods. The five different methods are non-compartment method, compartment method, bioanalytical method, mass spectrometry and population pharmacokinetic methods.

The factors that affect how a particular drug is distributed throughout the anatomy are: i) rate of blood perfusion; ii) permeability of capillary; iii) biological affinity of drug; iv) rate of metabolism of drug and; v) rate of renal extraction. Drugs may bind to proteins sometimes. Single compartment models were developed for:

i) first-order absorption with elimination; ii) fourth order absorption with elimination; (iii) second order absorption with elimination; iv) Michaelis-Menten absorption with elimination and; v) Reactions in circle Absorption with elimination. A system of n simple reactions in circle was considered. The conditions when subcritical damped oscillations can be expected are derived. A model was developed for cases when absorption kinetics exhibit subcritical damped oscillations can be expected. The solution was developed by the method of Laplace transforms. The solution for dimensionless concentration of the drug in single compartment for different values of rate constants and dimensionless frequency are shown in Figures 8.10 – Figures 8.14. The drug profile reaches a maximum and drops to zero concentration after a said time. The fluctuations in concentration depends on the dimensionless frequency resulting from the subcritical damped oscillations during absorption.

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At low frequencies the fluctuations are absent. As the frequency is increased the fluctuations in concentration are pronounced. The frequency of fluctuations were found to increase with increase in frequency of oscillations during absorption.

A two-compartment model for absortion with elimination is shown in Figure 7.15. The concentration that has diffused to the tissue region in the human anatomy is also accounted for in addition to the concentration of drug in the blood plasma. The model equation for concentration of drug in the tissue is found to be a ODE of the second order with constant coefficients (8.79). The model solution is given in Eq. (8.81) and obtained by the method of complementary function and particular integral.

Software has been developed for rhe implementation of the pharmacokinetic models on the personal computer.

Ratio control is used in keeping the reactor adiabatic during the manufacture of flue gas. Heat of reactions from Boudard reaction and oxidation of carbon reactions are made to cancel out each other by control of the ratio of the CO2/air mixture. SPC, statistical process control

methods are discussed including Deming‟s quality principles, QIT, quality improvement teams. IMC, internal model control uses the mathematical model developed for the process. Perfect control is achieved when model transfer function, M(s) is the reciprocal of process transfer function, Gp(s). A filter has to be added to make the system realizable. This keeps the

numerator of the transfer function of the combined system at a lower degree of polynomial compared with the degree of the polynomial of the denominator of the transfer function of the system. Inversion of a process model alone may not be sufficient for good control. In order for the controller to be stable and realizable the process transfer function must be factorized. Examples are given. In order to make M(s) proper the order of the filter in some cases is increased.

A lead/lag controller was considered by use of a model based transfer function as given. Inverse response is expected for the process. A first order filter was added to make the system more „proper‟. The order of the filter was increased to two. Feedforward control is different from feedback control. During feedforward control the load or disturbance is measured or gauged from other considerations and control action taken accordingly. Carrier corporation has patented [11] a feedforward control system for absorption chillers. The feedforward control method comprise of the following steps; (i) determine the disturbance transfer function; (ii) determine the capacity valve transfer function; (iii) measure the occurred disturbance; (iv) implement the feedforward control function. The block diagram for feedforward control of absorption chillers is shown in Figure 7.11. An example of feedforward controller for a resulation of fuel supply to a furnace was discussed.

Estimation and control of polymerization reactors was discussed. Control of polymerization of reactors is a difficult task because of the exothermic nature of the polymerization reactions and higher viscosities of monomer/polymer syrups encountered in the reactor. The objective of the reactor control is to obtain superior product quality. Quality is characterized by different parameters. Good structure-property relations are needed for devising control strategies based on measurement of end-use property values. Technical hurdles during the development of mathematical models for polymerization reactors are the nonlinear equations encountered, sensitivity to impurities etc. Feedback control of a variable is not possible if the parameter cannot be measured or estimated. In Figure 8.13 is shown the scheme for possible feedback control configurations. The scheme comprises of state-model, subsystem 1, subsystem 2 and the measurement equations. State estimation techniques have

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Preface xv been developed to provide estimates of the state variables even in cases where they cannot be obtained by direct measurement. Estimation techniques such as Kalman filter, Weiner filter and extended Kalman filter may be used. In non-linear estimation problems the objective may be to minimize the average squared errors in the initial estimate in the process model and in measurement device.

Neural networks can be used to approximate any reasonable function to any degree of required precision. Three different architectures for ANNs, artificial neural networks are possible. The behavior of each unit in time can be described using either differential equations or discrete update equations. The use of ANNs to control a distillation column is discussed. Robotics is introduced. Mathematics used to describe positions and orientations in 3 dimensional space is reviewed. Kinematics of mechanical manipulators are discussed. Forces, velocities and moments are discussed. Inverse kinematics of the manipulator with both the geometric and algebraic methods of solution is outlines. Trajectory analysis of end effector and control strategy for robots are outlined.

Submarine nanorobots are being developed for use in branchy therapy, spinal surgery, cancer therapy, etc. Nanoparticles have been developed for use in drug delivery systems and for cure in eye disorders and for use in early diagnosis. Research in nanomedicine is under way in development of diagnostics for rapid monitoring, targeted cancer therapies, localized drug delivery, and improved cell material interactions, scaffolds for tissue engineering and gene delivery systems. Novel therapeutic formulations have been developed using PLGA based nanoparticles. Nanorobots can be used in targeted therapy and in repair work of DNA. Drexler and Smalley debated whether „molecular assemblers‟ that are devices capable of positioning atoms and molecules for precisely defined reactions in any environment is possible or not. Feynman‟s vision of miniaturization is being realized. Smalley sought agreement that precision picking and placing of individual atoms through the use of „Smalley-fingers‟ is an impossibility. Fullerenes, C60, are the third allotropic form of carbon. Soccer

ball structured, C60, with a surface filled with hexagons and pentagons satisfy the Euler‟s law.

Fullerenes can be prepared by different methods such as: (i) first and second generation combustion synthesis; (ii) chemical route by synthesis of corannulene from naphthalene. Rings are fused and the sheet that is formed is rolled into hemisphere and stitched together; (iii) electric arc method. Different nanostructuring methods are discussed. These include: (1) sputtering of molecular ions;(2) gas evaporation; (3) process to make ultrafine magnetic magnetic powder; (4) triangulation and formation of nanoprisms by light irradiation ; (5) nanorod production using condensed phase synthesis method; subtractive methods such as; (6) lithography; (7) etching; (8) galvanic fabrication; (9) lift-off process for IC circuit fabrication; (10) nanotips and nanorods formation by CMOS process; (11) patterning Iridium Oxide nanostructures; (12) dip pen lithography; (13) SAM, self assembled monolayers; (14) hot embossing; (15) nanoimprint lithography; (16) electron beam lithography; (17) dry etching; (18) reactive ion etching; (19) quantum dots and thin films generation by; (20) sol gel; (21) solid state precipitation; (22) molecular beam epitaxy; (23) chemical vapor deposition; (24) CVD; (25) lithography; (26) nucleation and growth; (27) thin film formation from surface instabilities; (28) thin film formation by spin coating;(29) cryogenic milling for preparation of 100-300 nm sized titanium; (30) atomic lithography methods to generated structures less than 50 nm; (31) electrode position method to prepare nanocomposite; (32) plasma compaction methods; (33) direct write lithography; (34) nanofluids by dispersion. Thermodynamic miscibility of nanocomposites can be calculated from the free energy of

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mixing. The four thermodynamically stable forms of Carbon are diamond, graphite, C60,

Buckminster Fullerene and Carbon Nanotube. 5 different methods of preparation of CNTs, carbon nanotubes were discussed. Thermodynamically stable dispersion of nanoparticles into a polymeric liquid is enhanced for systems where the radius of gyration of the linear polymer is greater than the radius of the nanoparticle. Tiny magnetically-driven spinning screws were developed. Molecular machines are molecules that can with an appropriate stimulus be temporarily lifted out of equilibrium and can return to equilibrium in the observable macroscopic properties of the system. Molecular shuttle, molecular switches, molecular muscle, molecular rotors, molecular nanovalves are discussed. Supramolecular materials offers alternative to top-down miniaturization and bottom-up fabrication. Self-organization principles holds the key. Gene expression studies can be carried out in biochips. CNRs are a new generation of self-organizing collectives of intelligent nanorobots. This new technology includes procedures for interactions between objects with their environment resulting in solutions of critical problems at the nanoscopic level. Biomimetic materials are designed to mimic a natural biological material. Characterization methods of nanostructures include SAXS, small angle X-ray scattering, TEM, transmission electron microscopy, SEM, scanning electron microscopy, SPM, scanning probe microscope, Raman microscope, AFM atomic force miscroscopy, HeIM helium ion microscopy.

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A

BOUT THE

A

UTHOR

Dr. Kal Renganathan Sharma received his B.Tech. in chemical engineering from Indian Institute of Technology, Chennai, India in1985 and MS and PhD in chemical engineering from West Virginia University, Morgantown, WV in 1987 and 1990 respectively. He underwent post doctoral research training at the lifesciences and microgravity space lab, Clarkson University, Potsdam, NY under the guidance of former chair and Prof. R. Shankar Subramanian. He completed an industrial post doctoral research training with Monsanto Plastics Technology, Indian Orchad, MA. There he was told that he was on their “fast track” to become a research fellow. He has obtained cash prizes for exemplary work from Monsanto Plastics, Indian Orchad, MA, SASTRA University, Thanjavur, India and Prairie View A & M University, Prairie View TX. As a naturalized US citizen he obtained a ministry of human resources and development permit to serve as Professor at deemed universities in India.

He is sole author of 15 books and 7 book-chapters. Author of 53 Refereed Journal Articles, 553 Conference papers and 113 other presentations. He has instructed 2930 students in 109 courses. Service at HBCUs in this country, United States and deemed universities in India. Editorial Board Member of 7 journals. Fellow of Indian Chemical Society. Who‟s Who in America. Paper cited more than 300 times searcable by Google Scholar. Reviewed more than 60 journal articles. Developed new course and books in emerging areas. One article on “reactions in circle” in the open domain has been downloaded more than 1031 times and another article on nanorobots has been downloaded more than 980 times.

In recent years, he has been instructing courses in the Houston, TX area at Texas Southern University, Houston, TX, Lone Star College, North Harris, Houston, TX, Prairie View A & M University, Prairie View, TX. Prior to that he served at SASTRA University, Thanjavur, India, Vellore Institute of Technology, Vellore, India, George Mason University, Faifax, VA and West Virginia University, Morgantown, WV. Titles held include Adjunct Professor, Professor, Head, etc. He has a linked in profile at http://www.linkedin. com/pub/kal-sharma/48/b69/466.

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B

OOK

D

ESCRIPTION

Continuous Process Dynamics, Stability, Control and Automation is a modern first course on process control, instruments, process dynamics and stability. MS Excel spreadsheets are used in order to obtain solutions to non-linear equations when needed and closed form analytical solutions where possible are obtained using Laplace transforms and other methods. The solutions are presented in 210 figures and the book has 1319 equations. With a industrial controls market size of about 150 billion dollars and a chemical process industry market size of 3 trillion dollars the practioners can use this book to master techniques of P, proportional, PI, Proportional Integral, PD, Proportional Derivative feedback control, feedforword control, hybrid control, adaptive control, internal model control, ratio control, filtered real proportional integral derivative control, ANNs, artificial neural networks, SPC, statistical process control.

Control block diagrams are developed using MS Paint. Flavor for what is a continuous process is given using 18 process flow diagrams. Be it a feedback control of temperature in a mixing tank or a neural network design for a distillation column, the details and the big picture are both given. Pioneers who made this area possible such as Maxwell, Galileo, Sherwood, Levenspiel, Kalman, Laplace, Fermat, Damkholer, Newton, Fourier, Fick, Michaelis, Menten, Monod, Staudinger, Ziegler, Natta, Flory, Peclect, Bode, Nyquist, Biot, Bessel, Bernoulli (both father and son!), Euler, Stokes, Mach, Reynolds, Prandtl, Nusselt, Weiner, Hopf, Clapeyron, Clausius, Lorenz, Krebs mentioned where their theories were used in the analysis.

Coverage Includes:

 Separate Chapters are devoted to Continuous Polymerization Process Tech nology, Mathematical Models, Continuous Process Dynamics, Pharmacokinetics, Instruments and Nanorobots

 Multiplicity in Model solutions discussed with Examples

 Eleven different modeling approaches are discussed: Computer Simulations, Semi-Empirical; Shell Balance; State Space Models; Dimensionless Groups; Stochastic; Thermodynamic Analysis; Optimization Routines; Engineering Analysis and Derivation of Constitutive Laws

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 Control Lessons are drawn from Tormsdorff Effect, Regulation of Human Anatomical Temperature, 3 arm mechanical manipulator, centralized heating and cooling, Fukushima Earthquake, Deepwater Horizon Oil Spill

 Side by Side Comparison of Reactor Dynamics of Initiatiated and Thermal Polymerization

 Side by Side Comparison of Reactor Dynamics of CSTRs in Series and CSTR

 Dynamics of PFR and Wave Equation

 Continuous Mass Polymerization Process for Polystyrene, HIPS, High Impact Polystyrene, SAN, Styrene Acrylonitrile Copolymers, ABS, Acrylonirile, Butadiene and Styrene, PMMA, Poly Methyl Methacrylate, SMA, Styrene Maleic Anhyride, Condensation Processes for Nylon 6,6, Polyamide, Polyesters Gas Phase Catalytic Processes for Polyolefins and Ethylene Propylene Copolymer described as background

 Selectivity in Yield of Biodiesel over Glycerol in Consecutive-Competitive Reactions

 Computer Simulation of Centrifugal Separation of High Volume Oil and Water

 Control of Intermediate Yield of Chloroform during Chlorination Reactions in Series

 Supply Chain Robotics, Camera Drones

 Mathematical Artefact vs. Physically Reasonable Solution

 Overshoot Phenomena

 Frequency Analysis of Damped Wave Condution and Fourier Condution Equations

 Reactions in Circle Kinetics

 Michaelis and Menten Kinetics

 Nanorobots in Photodynamic Therapy of Alzheimers Disease

 Microwave Temperature Sensors, MEMS

 9 different Viscometers

 Pharmacokinetics of Styrene in Rats, Alchohol in Brain, Paclitaxel in cancer patients, Single Compartment and Two Compartment Models

 Subcritical Damped Oscillations

 Taylor Series Solution, Runge Kutta Method, Closed Form Solutions

 Intraocular Pressure

 Control Example in Toshiba Washing Machine

 Solar Aided Combined Cycle Power Plant

 Continuous Pharmaceutical Production

 Annular Plug flow Reactor for Single Layer Graphene Sheet Production

 Kalman Estimation and Control of Polymerization Reactor

 Bioartifical Pancreas

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Chapter 1

I

NTRODUCTION

1.1.

I

NDUSTRIAL

C

ONTROLS

-

M

ARKETS

The size of the worldwide market for industrial controls is expected to reach 150 billion dollars by the year 2019. Continuous processes have been selected over batch operations. Good reasons are lower cost, better product uniformity and sometimes result in less pollution to the environment. Process dynamics can be studied using desktop computers. Proactive process solutions can be offered. Stability analysis using a pencil and paper can lead to better perspectives on the range of operation of the process variables. Control and automation have grown tremendously with the advent of computers and programmable logic controllers, PLCs. More attention is paid by the lead engineer to the Plant start-up and Plant shut-down operations compared with steady-state operations of the plant. Transient analysis is not well studied. Transient behavior of chemical reactors, distillation columns, absorption towers, adsorption beds, extraction units and other unit operations needs to be better studied. Collegiate education methods have to keep pace with the developments in the field of industrial controls. Moore‟s law states that computing speed of microprocessors double every 18 months. Biological databanks double in size every 10 months. Mathematical methods for model development have been refined over centuries. The methods and means available to the engineer need be better utilized. Computer simulation and model development can be an integral part of an engineer‟s endeavors. The days when the effect of professionals who do mathematical modeling and computer simulation on the bottom line of the enterprise is only indirect are over. In the coming era the PW, Present Worth of chemical plants shall be higher because of the value added by an army of engineers and Ph.D. scholars who perform process dynamics studies and develop process models. They also develop control block diagrams, instrument the chemical plant with sensors and connect the sensor output using data acquisition to the desktop computer.

1.1.2. Chemical Process Industry

Over 70,000 different products are manufactured in the chemical process industry. Be it batch or continuous process for production the CPI is in constant need for control engineers, specialists who understand the dynamics, stability of different unit operations and processes.

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Automation is an identified goal in recent years. The size of CPI is estimated at 3 trillion US dollars. Depending on the boom and bust cycles of the economy the growth rate of CPI is a fraction of the GDP. The fraction is about 0.87. The first product made since industrial revolution was sulfuric acid. The lead chambers process was the industrial standard for two centuries. The largest volume product made today is polyethylene. The CPI is a science based industry.

The performance of the CPI in a given year depends on a number of factors. In 2013, cheap shale gas and a robust economy gave chemical producers a lift in terms of raw materials costs, utility costs, labor costs, tax and tariffs and interest on debt payments. President has urged Congress to increase the minimum wage rate for labor to $10.10 per hour. In Table 1.1 is listed the top 20 chemical producers in United States ranked by sales. Their assets are also provided. The products from the CPI include inorganic chemicals, plastics and petrochemicals, drugs and pharmaceuticals, soaps and detergents, paints and allied products, organic chemicals and phosphates, agricultural chemicals and miscellaneous chemicals.

1.2.

E

XAMPLES OF

C

ONTROL

A

PPLICATIONS

Here are some examples of why continuous process dynamics, stability, control and automation are important.

1.2.1. Supply Chain Robots

By the year 2019, the industrial controls market and industrial robotics market is expected to reach $147.7 billion worldwide. According to the market report prepared by Transparency market research in 2012 the size of the market is $102.2 billion. This would mean a compound annual growth rate of the industry, CGAR of 5.6% between 2013-2019. The largest end user is the automotive followed by the semi-conductor industry. The shipments of industrial robots in North America in the year 2000 were close to $1.02 billion. The discrepancy in the amount from $1.02 billion to $102.2 billion may be because of; (i) rapid growth rate and; (ii) differences in which machines that counts as robots amongst different regions of the world. Japan for example counts some machines as robots that in other parts of the world they do not.

Industrial robot was recognized as a unique device in the 1960s. CAD. Computer-aided design, computer aided manufacturing, CAM systems, sensors, controllers hooked to computers using data acquisition are emerging trends in industrial automation. The definition of an industrial robot according to Information Standards Organization (ISO) in standard ISO/TR/8373-2.3 is the following:

A robot is an automatically controlled, reprogrammable, multipurpose, manipulating machine with several programmable axes, which may be either fixed in place or mobile for use in industrial automation applications.

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Introduction 3 Table 1.1. Top 20 by Chemical Sales in this Country, United States

Rank Company Chemical Sales (Billions of US $) Assets of the Company (Billions of US $)

1 Dow Chemical, Midland, MI 57.1 69.5 2 ExxonMobil, Irving, TX 39.0 27.5 3 DuPont, Wilmington, DE 31.0 18.1 4 PPG Industries, Pittsburgh, PA 14.0 11.9 5 Chevron Phillips, The Woodlands, TX 13.1 10.5 6 Praxair, Danbury, CT 11.9 20.3 7 Huntsman, Salt Lake City, UT 11.1 9.2 8 Mosaic, Plymouth, MN 10.0 18.1 9 Air Products, Allentown, PA 9.7 16.2 10 Eastman Chemical, Kingsport, TN 9.4 11.8 11 Honeywell, Morristown, NJ 6.8 6.8 12 Celanese, Irving, TX 6.5 9.0 13 Ecolab, St. Paul, MN 6.5 19.6 14 Lubrizol, Wickliffe, OH 6.4 10.0 15 Ashland, Covington, KY 5.8 9.3 16 Dow Corning, Midland, MI 5.7 12.3 17 CF Industries, Deerfield, IL 5.5 10.7 18 Trinseo, Berwyn, PA 5.3 2.6 19 Momentive Specialty Chemicals, Columbus, OH 4.9 2.9 20 Occidental Petroleum, Los Angeles, CA 4.6 3.9

Per the Robotic Institute Association an industrial robot system is identified as the following:

An industrial robot system includes the robot(s) (hardware and software) consisting of the manipulator, power supply, and controller; the end-effector(s); any equipment, devices, and sensors required for the robot to perform its task; and any communications interface that is operating and monitoring the robot, equipment and sensors.

Robots can be programmed to move objects through the entire 3D, three dimensional workspace. The mechanical manipulator has a certain number of links, joints and end effector. The pitch, roll and yaw of the wrist can be controlled. Robots can be used to cause changes similar to what can be done using the motor skills of the human arm. The study of this field of study is called robotics. Stanford University developed a book used as textbook in collegiate course on Introduction to Robotics: Mechanics & Control, J. J. Craig since 1983. Robotics comprises of four major areas:

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(i) Mechanical Manipulation; (ii) Locomotion;

(iii) Computer Vision and; (iv) Artificial Intelligence.

Artificial intelligence development in the form of software is an attempt to synthesize human intelligence by computer programming and execution. Mechanical manipulation is an amalgamation of engineering mechanics, control theory and computer science. Progress made in robot programming languages can lead to more user friendly robots. Often times the interface between human and robot is the programming language. This is an important consideration in the design and operation of industrial robots. Library of robot-specific subroutines can be added. JARS, written in Pascal at NASA‟s Jet Propulsion Laboratory is an example. RAPID is a general-purpose language and software developed by ABB Robotics. The user is allowed to command specific sub-tasks of the goal directly. Manipulator programming takes some trial and error. Objects can be moved through 3D, three dimensional space using programming. VAL was written by Unimation in order to control robots. IBM developed AML. KAREl was developed by GMF robotics. Cartesian and interpolated motions can be affected. Vision systems can be used to specify the coordinates of an object of interest. Interaction with sensors is important. Some difficulties in programming robots are the congruence of model predictions and real time experience. This can lead to poor grasping of objects and collisions. Accuracy of manipulator is another worry. Force strategies needs to be developed for constrained motion. Manipulator programs are sensitive to initial conditions. Trajectory and velocity of arm depends on the initial conditions. Program segments need be tuned in a bottom-up programming approach. Changes in configuration can cause large arm motions. Errors in object location can also be causative in problems. Singularities can be identified by writing the Jacobian that can be used to relate the joint velocities to Cartesian velocities of the tip of the arm. For example, say the end effector is required to move with a certain velocity in Cartesian space. Inverse Jacobian can be used to calculate the joint rates from the Cartesian rates. For some values of the vector of joint angles, the jacobian becomes singular. Singularities in the boundaries can be found for most manipulators and have a loci of singularities inside the workspace.

1.2.2. Robotics as Collegiate Course

Industrial robots came about in the 1960s. The adoption of robotic equipment came about in the 1980s. In the late 1980s there was a pullback in the use of robots. Use of industrial robots has been found to be cheaper than manual labor. More sophisticated robots are emerging. Nanorobot drug delivery systems can be one such robot. 78% of the robots installed in the year 2000 were welding and material-handling robots. The Adept 6 manipulator had 6 rotational joints. The most important form of the industrial robot is the mechanical manipulator. The mechanics and control of the mechanical manipulator is described in detail in introductory robotics courses [Craig 2005]. The programmability of the device is a salient consideration. The math needed to describe the spatial motions and other

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Introduction 5 attributes of the manipulators is provided in the course. The tools needed for design and evaluation of algorithms to realize desired motions or force applications are provided by control theory. Design of sensors and interfaces for industrial robots is also an important task. Robotics is also concerned with the location of objects in three dimensional space such as its: (i) position and orientation; (ii) coordinate system and frames of reference such as tool frame and base frames; (iii) transformation from one coordinate system to another by rotations and translations.

Kinematics is the science of motion that treats motion without regard to the forces that cause it. In particular, attention is paid to velocity, acceleration of joints and acceleration of the end effector. The geometrical and time based properties of the motion are studied. Manipulators consist of rigid links that are connected by joints that allow relative motion of neighboring links. Joints are instrumented with position sensors which allow the relative motion of neighboring links to be measured. In case of rotary or revolute joints these developments are called joint offset. The number of independent position variables that would have to be specified in order to locate all parts of the mechanism is the number of degrees of freedom.

End effector is at the end of the chain of links that make up of the manipulator. This can be a gripper, a welding torch, an electromagnet, etc. Inverse kinematics is the calculation of all possible sets of joint angles that could be used to attain the given position and orientation of the end-effector of the manipulator. For industrial robots the inverse kinematic algorithm equations are non-linear. Solution to these equations is not possible in closed form. The analysis of manipulators in motion in the workspace of a given manipulator includes the development of the Jacobean matrix of the manipulator. Mapping from velocities in joint space to velocities in Cartesian space is specified by the Jacobean. The nature of mapping changes with configuration. The mapping is not invertible at points called singularities.

Dynamics is the study of actuator torque functions of motion of manipulator. State space form of the Newton-Euler equations can be used. Simulation is used to reformulate the dynamic equations such that the acceleration is computed as a function of actuator torque. One way to effect manipulator motion from here to there in a specified smooth fashion is to cause each joint to move as specified by a smooth function of time. To ensure proper coordination, each and every joint starts and stops motion at the same time. The computation of these functions is the problem of trajectory generation. A spline is a smooth function that passes through a set or via points. End effector can be made to travel in a rectilinear manner. This is called Cartesian trajectory generation.

The issues that ought to be considered during the mechanical design of manipulator are cost, size, speed, load capability, number of joints, geometric arrangement, transmission systems, choice and location of actuators, internal position and sensors. The more joints a robot arm contain the more dexterous and capable it will be. But it will also be harder to build and be more expensive. Specialized robots are developed to perform specified tasks and universal robots are capable of performing wide variety of tasks. Three joints allow for the hand to reach any position in three dimensional spaces. Stepper motors or other actuators can be used to execute a desired trajectory directly. This is called the linear positional control. Kinetic energy of the manipulator links can be calculated using the Langrangian formulation. Both the linear kinetic energy and rotational kinetic energies can be tracked.

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Table 1.2. State of Art in Robots over a 40 Year Period

Year Product

1974 World‟s First Microcomputer controlled Electric Industrial Robot, IRB6 from ASEA was delivered to a small mechanical engineering company in Southern Sweden.

1975 IRB6 – First Robot for Arc Welding 1977 First Robots installed in France and Italy

1979 IRB 60 – First Electrical Robot for Spot Welding; First Robot installed in Spain 1982 Robots introduced in Japan

1983 S2, New Control System. Outstanding, HMI, Menu Programming, TCP (Tool Center Point) and the Joy Stick introduced. Allows control of several axes.

1986 ASEA bought Trallfa Robot operations, Bryne, Norway. Trallfa launched the world‟s First Painting Robot in 1969. Sales boomed in the mid 1980s when the automotive industry started to paint bumpers and other plastic parts.

IRB2000 – 10 Kg Robot. First to be driven by AC motors. Large working range. Great accuracy.

1990/ 1991

ABB acquired Cincinnati Milacron, USA, Graco, USA (robotic painting), Rarsburg Automotive (electrostatic painting atomizers).

IRB 6000 – 200 kg Robot introduced. First Modular Robot that is the fastest and most accurate spot welding robot on the market.

Unique hollow wrist introduced on Painting Robots. Allows faster and more agile motion. 1994 S4 – Breakthrough in user friendliness, dynamic models, gives outstanding performance.

Flexible rapid language.

1996 Integrated Arc Welding Power Source in Robot Cabinet.

1998 Launch of Flex Picker Robot, the World‟s fastest Pick and Place Robot.

Robot Studio – First simulation tool based on virtual controller identical to the real one revolutionize off-line programming.

2000 Pick and Place Software Pick Master introduced. 2001 IRB7000 – First Industrial Robot to handle 500 kg. 2002 ABB – First company in World to sell 100,000 Robots

Virtual Arc – True arc welding simulation tool that gives robot welding engineers full „off-line‟ control of the MIG/MAG process.

IRB 6000 – Power Robot with bend over backwards flexibility. 2004 IRC5- Robot Controller. Windows interface unit.

2005 Launch of 55 new products and robot functions included with 4 new robots: IRB 660, IRB 4450, IRB1600, and IRB260.

The evolution of robots can be studied from the market experience of one leading manufacturer of robots for example, ABB robotics, Zurich, Switzerland. This shall be used later to place in perspective the increased interest in use of nanorobots in the hospital for drug delivery. The different products from ABB Robotics during a 30 year period are given below in Table 1.2.

1.2.3. Infeasibility of “Molecular Assemblers”

R.E. Smalley and K.E. Drexler debated on whether “molecular assemblers,” which are devices capable of positioning atoms and molecules for precisely defined reactions in any environment, are possible. In his book Engines of Creation: The Coming Era of

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Introduction 7 Nanotechnology, Drexler envisioned a world ubiquitous with molecular assemblers. These would provide immortality and lead to the colonization of the solar system. He received a Ph.D. from Massachusetts Institute of Technology (MIT) in 1991. He is also the CEO of Foresight Institute, Palo Alto, California. Smalley, a recipient of the Nobel Prize in chemistry in 1996 for his work on fullerenes, outlined his objections based on science to the molecular assembler idea and called it the “fat fingers” problem” or the “sticky fingers problem.” He was also worried about funding for nanotechnology due to the portrayed darker side of it. In Chemical and Engineering News, “Point–Counterpoint” column, an open letter from Drexler to Smalley was posted challenging Smalley to clarify the “fat fingers problem,” with a response from Smalley and three letters with Drexler countering and Smalley concluding the exchange.

Drexler sought clarification from Smalley on the fat fingers problem. He felt that like enzymes and ribosomes, the proposed molecular assemblers neither have nor need the “Smalley Fingers.” Drexler alluded to the long-term goal of molecular manufacturing and its consequences, which can pose opportunities and dangers to long-term security of the United States and the world. Theoretical studies and implementation capabilities are akin to the pre-Sputnik studies of spaceflight or the pre-Manhattan project calculations regarding nuclear chain reactions and are of more than academic interest. He referred to his 20-year history of technical publications in the area of chemical synthesis of complex structures by mechanically positioning reactive molecules and not by manipulating individual atoms.

The proposal was successfully defended in the doctoral thesis on Nanosystems: Molecular Machinery, Manufacturing, and Computation and is based on well-established physical principles. Smalley responded with an apology should he have offended Drexler in his article in Scientific American in 2001. His call for using “Smalley Fingers” is impossible. A “Smalley Finger” type of molecular assembler tool will never work. Smalley pointed out the infeasibility of tiny fingers placing one atom at a time. This is also applicable to placing larger, more complex building blocks. As each incoming reactive molecule building block has multiple atoms to control during the reaction, more fingers are needed to ensure they do not go astray. Computer-controlled fingers will be too fat and too sticky for providing the control needed. Fingers cannot perform the chemistry necessary. He called attention to the mention of enzymes and ribosomes needed in the reaction medium. He quarreled with the vision of self-replicating nanobot. Is there a living cell inside the nanobot that cranks these out? Water is needed inside the nanobot with the necessary nutrients for life. How do the nanobot pick the right enzyme and join in the right fashion? How do the nanobot perform error detection and error correction? He worried about the scope of the chemistry that the nanobot could perform. Enzymes and ribosomes need water to be effective. He mentioned that although biology is wondrous, a crystal of silicon, steel, copper, aluminum, titanium, and other key materials of technology could not be produced by biology. Therefore, without these materials how could a nanobot manufacture a laser, ultrafast memory and other salient components of modern society?

Drexler applauded Smalley‟s goal of debunking nonsense in nanotechnology. He sketched the fundamental concepts of molecular manufacturing. He referred to Feynman‟s after-dinner visionary talk in 1959, discussed in Sec. 1.2, and nanomachines building atomically precise products. Feynman‟s nanomachines were largely mechanical and not biological. In order to understand how a nanofactory system could work, he considered a conventional factory system. Some of Smalley‟s questions reach beyond chemistry to systems

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engineering and to problems of control, transport, error rates, and component failure and to answers from computers, conveyors, noise margins, and failure-tolerant redundancy. Nanofactories contain no enzymes, no living cells, and no replicating nanobots, but they do use computers for precise control, conveyors for parts transport, and positioning devices of assorted sizes to assemble small parts into large parts when building macroscopic products. The smallest devices position molecular parts to assemble structures through mechano-synthesis or machine-based chemistry. Conveyors and positioners bring reactants together unlike solvents and thermal motion. Positional control enables a strong catalytic effect by aligning reactants for repeated collisions in optimal geometries at vibrational frequencies greater than terahertz. Positional control can lead to voiding unwanted side reactions. From transition state theory, for suitably chosen reactants, positional control will enable synthetic steps at megahertz frequencies with reliability approaching that of digital switching operations in a computer. When molecules come together and react, their atoms, being sticky, stay bonded to neighbors and thus do not need sticky fingers to hold them. Direct positional control of reactants is revolutionary and achievable. Mechanosynthetic reactions and its field have parallels in the field of computational chemistry. The flourishing of nanotechnology in 2003 suggests a bottom-up strategy using self-assembly. It used to be scaling down microscopic machines in 1959.

Progress toward molecular manufacturing is achieved by research in computational chemistry, organic synthesis, protein engineering, supramolecular chemistry, and scanning probe manipulation of atoms and molecules. Scaling down moving parts by a factor of one million results in multiplication of their frequency of operation by the same factor. Progress in the United States on molecular manufacturing has been impeded because of the illusion that it is infeasible. He called for augmentation of nanoscale research with systems engineering effort and achievement of the grand vision articulated by Richard Feynman. Smalley concluded by observing that Drexler left the talk about real chemistry and went to the mechanical world. He felt that precise chemistry could not be made to happen as desired between two molecular objects with simple mechanical motion along a few degrees of freedom in the assembler fixed frame of reference. It was agreed that a reaction would be obtained when a robot arm pushes the molecules together but it may not be the reaction desired. More control is needed than mentioned about molecular assemblers. A molecular chaperone is needed that serves as catalyst. Some agent such as an enzyme is needed. A liquid medium such as water is needed to complete the desired chemical reactions. Smalley recalled a talk on nanotechnology he gave to 700 middle and high school students. The students were asked to write an essay on “Why I am a Nanogeek.” Smalley read the top 30 essays and he picked his favorite five. Half assumed that self-replicating nanobots were possible. What if the self-replicating nanobots fill the world, was the worry of some. However, they have been misinformed.

1.2.4. Trends in Industrial Robots

Industrial robots are increasingly used. According to Prof. D. Rus, director of MIT‟s computer science and Artificial Intelligence Laboratory a tipping point in the field of robotics has been reached. There is a paradigm shift from robots as job terminators to robots as job creators. Companies report more productivity per worker with more usage of robots.

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Introduction 9 Universal robot, specialized robots, numerically controlled milling machines are increasingly used. Electronics industry is reaching the trillion dollar mark rapidly. Automotive and semiconductor industries increasingly use the robots. Robots can be used in miniaturization operations. Nanotechnology products are expected to have a market size of 3 trillion dollars by the year 2015. Microarrayer and nanoarrayers are expected to be increasingly used in genome sequencing and making biochips. Mechanical micro spotting and ink jet printing use principles of robotics and automation.

Apple is planning on investing $10.5 billion in supply chain robotics and lasers and new technology ranging from assembly robots to milling machines. These machines are used for mass production of iphones, ipads, and smart phones. Amazon has recently announced the development of drones for rapid door delivery of ordered items. With a service called „Prime Air‟, by 2015 drones are expected to be used in order to deliver packages less than 30 minutes. Pizza deliveries are also expected. Internet access is expected to be delivered by drones to remote areas. Robots have been made with a spectrum of capabilities. Researcher at University of California at Berkeley has developed a robot for drying and folding clothes from the laundry. In 2000, 78% of installed robots were used for welding or material handling. Samsung, Hewlett Packard, Apple and other competitors use robots for spray painting. Robotic Equipment is used to polish new iPhone and carve plastic MacBook‟s aluminum body. Other consumer electronics investments are $ 22 billion by Samsung, $3.7 billion by Hewlett Packard and $3.95 billion by Sony Corp. Apple has hired robot experts and engineers to oversee operation of high-end manufacturing equipment.

When the baby-boomers reach the retirement age in the near future there is a shortage expected in skilled workers. Developments like Baxter that can be used to perform tasks of two workers can increase the usage of robots. Robots can be used to unassemble pipe from conveyor. Marlin steel use robots in order to produce wire baskets and sell it to car makers and pharmaceutical firms. Lear a major auto-parts maker near Detroit, MI, with nearly $15 billion in annual revenue uses robots developed by Universal Robotics to help screw together seats and put together electronics dashboards. Sensors, instrumentation and industrial automation are expected to be a tidy addition to modern chemical plants. Key Competitors identified in the market report by Transparency Market Research are: (i) Denso Wave Inc; (ii) FANUC Corp.; (iii) KUKA AG; (iv) Yaskawa Electric Corp; (v) Toshiba Machine Corp; (vi) Yogakoaw Electric Corp; (vi) ABB Ltd. (vii) Honeywell Intl.; (viii) Emerson Electric; (ix) General Electric; (x) Invensys PLS; (xi) Mitsubishi Electric; (xii) Rockwell Automation; (xiii) Siemens; (xiv) Omron Corp; (xv) Schneider Electric; (xvi) Kawasaki Robotics.

1.2.5. Drones

Robo-roo is a robot built by engineers in Germany that can be made to hop like a kangaroo. The kangaroo's jump is unique and contains a mechanism where the jump speed can be controlled in an efficient manner. The Achilles tendon stretches from the kangaroo's head to its calf. Festo scientists in Germany have devised an elastic spring that can be used in order to enable the robo-roo's functionality much like the real Achilles tendon. The kinetic energy of the kangaroo is absorbed by the elastic spring when the robo-roo lands. The next jump is powered by the energy released from the spring. Electronic circuitry are being developed to store and release energy in a more efficient and controlled manner. The robo-roo

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