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For various reasons, it is important to optimize processes so that a chosen quantity, known as the objective function, is maximized or minimized. For example, the output, profit, productivity, product quality, etc., may be maximized, or the cost per item, investment, energy input, etc., may be minimized. The success and growth of industries today is strongly based on their ability to optimize designs and systems. With the advent in the recent years of new materials, such as composites and ceramics, and new manufacturing processes, several traditional industries (e.g., steel) have faced significant challenges and, in some cases, diminished in size, while many new fields have emerged. It is important to exploit new techniques for product improvement and cost reduction in traditional and new industries. Even in an expanding area, such as consumer electronics, the prosperity of a company is closely connected to its ability to apply optimization to new and existing process and system designs. Consequently, engineering design, which has always been important, has become increasingly coupled with optimization [47]. Optimization is a significant tool in engineering for determining the best, or optimal, value for the decision variable of a system. Energy engineering is a field where optimization plays a particularly important role. Engineers involved in thermal energy engineering, for instance, are required to answer the questions such as What processes or equipment should be selected for a system, and how should the parts be arranged for the best outcome? What are the best characteristics for the components (e.g., size, capacity, cost)? What are the best process parameters (e.g., temperature, pressure, flow rate and composition) of each stream interacting with the system? In order to answer such questions, engineers are required to formulate an appropriate optimization problem. Proper formulation is usually the most important and sometimes the most difficult step in optimization. To formulate an optimization problem, there are numerous elements that need to be defined, including system boundaries, optimization criteria, decision variables and objective functions. Sahoo [60] carried out an exergoeconomic analysis and optimization of a cogeneration system which produces 50 MW of electricity and 15 kg/s of saturated steam at 2.5 bar. He optimized the unit using exergoeconomic principles and evolutionary programming, and showed that the cost of electricity production is 9.9% lower for the optimum case in terms of 43PDF Image | Energy Systems for Multigeneration Purposes
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