Chilled Water Pump trouble shooting by A I : A case study

CHILLED WATER PUMP TROUBLE-SHOOTING BY A.I.: A CASE STUDY

Priyabrata Adhikary

, Ashok Kumar

, Sumit Bandyopadhyay

and Asis Mazumdar

1

New Horizon College of Engineering, Bangalore, Karnataka, India

2

Blue Star Limited, Kolkata, West Bengal, India

3

School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India E-Mail: priyabrata24@gmail.com

ABSTRACT

Artificial intelligence (Artificial Neural Network-ANN, Fuzzy Expert System - FES, Genetic Algorithm - GA etc.) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems in HVAC industry. It can learn from previous data, are fault tolerant, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at a very high speed. It has been used in diverse applications in controlling system, robotics, manufacturing, optimisation, signal processing etc. This study presents application of ANN in HVAC chilled water pump (Various Global Chilled Water Pump Manufacturers: Grundfos, KSB, Armstrong, Kirloskar etc.) trouble shooting. In all those models, multiple hidden layer architecture has been used. Errors reported in these models are within acceptable limits, which suggest that AI or ANN can be used for such modelling. Good agreement was found between the ANN forecast results and actual pump manufacturer data (not shown here for the company privacy policy) for the Chilled Water Pump trouble shooting. To the best of the author’s knowledge these novel approaches for application of Artificial Neural Network (ANN)in chilled water pump (HVAC) trouble shooting problem is absent in fluid mechanics literature due to its assessment complexity.

Keywords: artificial neural network, chilled water pump, HVAC, air conditioning, refrigeration.

1. INTRODUCTION

Chilled water has been a primary medium for the transfer of heat from building coils to the refrigeration system since the beginning of heating, ventilating, and air-conditioning design. A typical HVAC system consists of plant equipment (chillers, AHUs, Pumps etc.) which transfer energy via air, water or a refrigerant to air distribution systems. Generally, air is transported through ductwork while water and refrigerants are distributed through pipe work. The entire process is energy intensive - the main users of this energy being the HVAC plant: chillers, AHUs, FCUs, fans and chilled water pumps.

Figure-1. Chilled Water Pumping System.

The chilled water pump (Manufacturers: Grundfos, KSB, Armstrong, Kirloskar etc.) always pumps the difference between the suction and discharge heads. If the suction head increases, the pump head will decrease to meet the system requirements. If the suction head decreases the pump head will increase to meet the system

requirements. A pump always pumps a combination of head and capacity. These two numbers multiplied together must remain a constant. In other words, if the head increases the capacity must decrease. Likewise, if the head decreases, the capacity must increase. The pump will pump where the pump curve intersects the system curve. If the pump is not meeting the system curve requirements the problem could be in the pump, the suction side including the piping and source tank, or somewhere in the discharge system. Most pumps are oversized because of safety factors that were added at the time the pump was sized. This means that throttling is a normal condition in most plants, causing the pump to run on the left-hand side of its curve.

Figure-2. Chilled Water Pumps (HVAC Plant Room).

Logic (FL); Neural Networks (NN), including Neural Computing (NC); Genetic Algorithms (GA); Machine Learning (ML); Probabilistic Reasoning (PR) etc. [1-50].

2. METHODOLOGY ADOPTED

ANNs have been used successfully in several applications namely: classification, forecasting, control systems, optimization and decision making etc. In the brain, there is a flow of coded information (using electrochemical media, the so-called neurotransmitters) from the synapses towards the axon. The axon of each neuron transmits information to several other neurons. The neuron receives information at the synapses from many other neurons. It is estimated that each neuron may receive stimuli from as many as 10,000 other neurons.

Figure-3. Biological Neuron to Artificial Neuron.

Groups of neurons are organised into subsystems and the integration of these subsystems forms the brain. It is estimated that the human brain has got around 100 billion interconnected neurons. In addition, neural networks are fast, fault tolerant, robust and noise immune. A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the human brain in two respects; the knowledge is acquired by the network through a learning process, and inter-neuron connection strengths known as synaptic weights are used to store the knowledge [51-60].

3. THEORY AND CALCULATION

Artificial neural network (ANN) modellings may be used as an alternative process in engineering analysis and decision making. It operates as a ``black box'' model, requiring no detailed information about the process. Instead, it learns the relationship between the input and the controlled and uncontrolled variables by studying previous data. In addition, ANNs has ability to handle large and complex systems. The typical multilayer feed forward neural network generally consists of an input layer, few hidden layers and an output layer. In its simple form, each single neuron is connected to other neurons of a previous layer by adaptable synaptic weights. The feed forward back-propagation (FFBP) algorithm is one of the most popular and powerful learning algorithms in neural networks. The training of all patterns of a training data set is called an epoch. The error is expressed by the root-mean-square value (RMS) [61-76].

Figure-4. ANN Information processing.

4. C.W.P. TROUBLE SHOOTING: A CASE STUDY It is not a secret that a chilled water pump that runs at peak efficiency uses less fuel, experiences less downtime and costs less to operate. The time we spend maintaining our pump is actually an investment in its lifetime performance and value. In fact, there are many ways that a diligently maintained pump can reduce costs, while increasing efficiency. Managing adequately the building energy demands has always been a struggle for facility managers.

This study considered assessing the chilled water pump trouble shooting of commercial buildings as a function of pump RPM. The dataset comprises 209 instances and 08 attributes or features (Serial No., Failure Class, Component Number, Machine Measurement Support Location, Frequency of Measurements, Present Measurement, Earlier Measurement, Filter Data) aiming to predict 01 real valued responses or outcomes (Pump RPM).

5. RESULT AND DISCUSSIONS

From this study of assessing the chilled water pump trouble shooting of commercial buildings as a function of RPM, the results obtained in training and validation are (R = 0.73) shown below. Despite the disadvantages of ANN known as black box model, ANN can simulate the pattern. Based on the pattern recognition of input and output, ANN can model the nonlinear processes.

Figure-6. Validation Dataset for CWP - ANN.

This study presents application of ANN in HVAC system design problem and equipment trouble shooting. In all those models, multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits (R= 0.73).

Figure-7. Test Dataset for CWP - ANN.

Good agreement was found between the ANN forecast results and actual pump manufacturer data (not shown here for the company privacy policy) for the Chilled Water Pump trouble shooting (R=0.73).

Figure-8. Combined - Test Dataset for CWP - ANN.

Figure-9. ANN Performance for CWP - MSE.

Figure-10. ANN Training State Curve - CWP.

6. CONCLUSIONS

The ANN modelling presented here can predict the chilled water pump trouble shooting of commercial buildings with acceptable accuracy. It is certainly more economical to be able to investigate the behaviour of energy consuming systems without having to construct and experiment on several systems or use expensive models and trouble shooting. The application of ANNs have shown that it is possible to model such systems with a minimum amount of input data, thereby providing the designer of such systems with the flexibility to test several systems quickly. The significant reduction in estimation times is the major benefit of the present method. We are planning to extent the present work into other areas of the subject to create a much versatile simulation tool.

ACKNOWLEDGEMENT

The authors wish to thank Jadavpur University, Kolkata and N.H.C.E. Bangalore for the valuable technical literature, MATLAB software and project data support (Machine Learning Repository Datasets). The authors declare that there is no conflict of interests.

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