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Simulation, Control, and Optimization of Water Systems in Industrial Plants 467

and operating strategy on key performance indicators such as production,
yield, product quality, energy efficiency, and effluent discharge and eco-
nomics, and thus optimise the process design or operation.

12.1.1.1 Benefits of Modeling, Simulation, and Analysis
According to practitioners, modeling, simulation, and analysis are some of the
most frequently used operations research techniques (Maria, 1997). When
used judiciously, modeling, simulation, and analysis make it possible to:
• Obtain a better understanding of the system by developing a mathemat-
ical model of a system of interest and observing the system’s operation in
detail over long periods of time.
• Test hypotheses about the system for feasibility.
• Compress time to observe certain phenomena over long periods or
expand time to observe a complex phenomenon in detail.
• Study the effects of certain informational, organizational, environmental,
and policy changes on the operation of a system by altering the system’s
model; this can be done without disrupting the real system and signifi-
cantly reduces the risk of experimenting with the real system.
• Experiment with new or unknown situations about which only weak
information is available.
• Identify the “driving” variables—ones that performance measures are
most sensitive to—and the interrelationships among them.
• Identify bottlenecks in the flow of entities (e.g., material, energy) or
information.
• Use multiple performance metrics for analyzing system configurations.
• Employ a systems approach to problem solving.
• Develop well-designed and robust systems and reduce system
development time.

12.1.1.2 What are Some Pitfalls to Guard Against in Simulation?
Simulation can be a time-consuming and complex exercise, from modeling
through to output analysis that necessitates the involvement of resident
experts and decision makers in the entire process (Maria, 1997). Following
is a checklist of pitfalls to guard against.
• Unclear objectives
• Using simulation when an analytic solution is appropriate
• Invalid model
• Simulation model too complex or too simple
• Erroneous assumptions