Operator Training Simulator (OTS)

Intricate and highly interacting production processes pose tough challenges in maintaining safe and efficient production. An inevitable need of skilled operators to increase the safety and the productivity is not new to the chemical industry.

Consequently, the training of operators is considered as a very important activity in the chemical industry. An OTS provides an alternative to train operators without actually endangering the plant and personnel. In complex industries, where safety is paramount, identification of key factors that can degrade/enhance safety is a must (Park et al., 2004). Yang et al. (2001) reported that significant percentage of property losses in the hydrocarbon processing industries is due to operational errors or process upsets. This reinforces the need of OTS to develop and retain the operators’ skills.

To ensure that operators retain the knowledge, skills and remain competent to control processes in emergency conditions, they should be provided with training opportunities to develop and sustain their capabilities. On-job training is often costly, risky and incomplete as some emergency situations may not arise during the training

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session. Therefore, training using an OTS is crucial. Manca et al. (2013) discussed the benefits of integrating and interlinking a dynamic process simulator with a dynamic accident simulator in OTS training. According to Shepherd (1986), as long as operators are working on the complex plants and equipments, development and administration of their training are required. He reported that a training technique implies adopting one or more of the following: teaching plant and process knowledge, on-job instruction, training on a simulated plant and development.

The paradigm of one of these training methods alone may not be effective.

Shepherd (1986) recommended adopting all the above mentioned for a comprehensive training program. In the chemical industry, especially in the case of continuous processes, OTS has been used (Balaton et al. 2013). An increasing number of chemical companies use OTS aiming to train the operating staff on handling different malfunctions and infrequently occurring modes of operation.

Other applications of OTS include assessment of operators’ skills, supporting engineering tasks such as investigating alternate control mechanisms and performing safety tests without any risk to the real system (Fürcht et al. 2008; Rey 2008).

In addition to classroom teaching and field training, simulator training is also significant in the operator training program (Jayanthi et al. 2011). OTS is safe and reliable to train control room operators as long as it can provide a credible simulation of the real plant (Drozdowicz et al. 1987). Figure 1.1 illustrates the comparison between real plant training and simulator training.

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Figure 1.1 Comparison between real plant training (top) and simulator training (bottom).

Typically, the simulator includes a replica of the plant’s control room (hardware, interfaces, screens, printers, etc.) and a software emulation of the distributed control system to be coupled with the process models (Spanel et al., 2001). The general configuration of the full scope OTS for any chemical plant can be as shown in Figure 1.2.

Figure 1.2 General configuration of full scope OTS.

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In this figure, the instructor station provides the interface for the instructor to insert faults, monitor and control the training session while the trainee operators use the operator station. The trainee operator station has generic process control system schematics that enable point and click access to the controller faceplates. The instructor station functionalities reported by Dudley et al. (2008) are: scenario creation and imparting malfunctions/upsets into the process model, monitoring and trending of any plant variable, training and evaluation of operators, run/pause/resume and load/save capabilities, Snapshots, backtracks and speed control (i.e. fast/slow capabilities), and storing of data on plant variables, which can be used for post-scenario reviews.

In addition, preliminary hazard and operability study (HAZOP) analysis is carried out to assist a trainee find out causes and possible solutions. HAZOP is a structured and systematic examination of a non-existing or existing process in order to identify and evaluate problems that may indicate risks to process or personnel, or reduce the efficient process operation. OTS needs a suitable process model that can reflect the process as real as possible. Therefore, it is important to carry out a realistic simulation, determine optimal conditions and develop a complete PWC structure. In this work, OTS uses the same process model as it is used in MOO and PWC study.

The optimal parameters determined from MOO of the biodiesel process is used in PWC and OTS study. Following section presents the merits of MOO.

9 1.4 Multi-objective Optimization (MOO)

Once process extablished and simulated, MOO is used to determine the optimal parameters of the decision variables. MOO is also used to compare the two process alternative. In general, MOO is the method of finding optimal values of the parameters for the maximization or the minimization of given objectives within prescribed constraints. MOO involves maximizing or minimizing multiple objective functions subject to a set of constraints, for example analyzing design tradeoffs, selecting optimal product or process designs, or any other application where an optimal solution with tradeoffs between two or more conflicting objectives is desired. Optimization plays crucial role in reducing material and energy requirements as well as the waste formation in chemical processes. It is also essential in determining better design and operation of chemical processes. Many chemical processes involve several objectives, most of which are conflicting in nature. Several chemical processes have many variables with complex inter-relationships;

optimizing these objectives is challenging. MOO is needed to determine the optimal solutions in such applications. MOO is used to find a set of nondominated solutions for two or more objectives simultaneously (Sharma and Rangaiah, 2013b).

Consequently, in last few years, a significant attention has been given to MOO.

MOO has been vastly used to optimize chemical processes having conflicting objectives such as conversion, selectivity, yield, energy, environment and safety in addition to economic objectives. Evolutionary algorithms, such as non-dominated sorting genetic algorithm (NSGA-II), are popular methods to generate Pareto optimal solutions for a multi-objective optimization problem. It is a stochastic optimization method that that generates and uses random variables. Other stochastic optimization

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methods include simulated annealing, quatum annealing, swarm algorithms, differential evaluation etc. NSGA-II has become popular approach and it can be seen from its wide applications.

The main advantage of evolutionary algorithms (EA) is that the EAs are inherently stochastic in nature, and thus they generate the Pareto front when applied to solve multi-objective optimization problems. Multi-objective optimization problems can be solved using Genetic algorithm and its improved versions to find set of points on the Pareto front. The major drawbacks of multi-objective evolutionary algorithms (MOEAs), such as NSGA, that use non-dominated sorting and sharing are: (1) their computational complexity O(MN³) (where, M is the number of objectives and N is the population size), (2) their non-elitism approach (absence of elitism as opposed to NSGA-II, where parents are selected from the population by using binary tournament selection based on the rank and crowding distance), and (3) the necessity to specify a sharing parameter (Deb et al., 2002). Deb et al. (2002) proposed extension of NSGA i.e., NSGA-II, which alleviates all of the above three difficulties; hence, NSGA-II is used later in this study. After finding the better process out of the two alternative processes, a suitable PWC structure should be implemented; this is explained below.