Narrow Results

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams–Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. Multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

The solution set of any multi-objective optimization problem can be expressed as an approximation set of Pareto front. The number of solution candidates in this set could be large enough to cover the entire Pareto front as a general belief. However, among the sufficiently close points on the objective space, almost same accurate solutions can obtain. Hence, in this set, it is possible to eliminate some of the solutions without detriment to the overall performance. Therefore, in this research, the authors propose a population size reduction method which systematically reduced the population size based on the distance and angle relations between any consecutive solutions. The results are evaluated based on two-objective benchmark problems and compared with the results of NSGA-II algorithm with respect to three different performance evaluation metrics.

This paper developed multi-objective optimization design of proportional–derivative (PD) and proportional–integral–derivative (PID) controllers for seismic control of high-rise buildings. The case study is an 11-story realistic building equipped with active tuned mass damper (ATMD). Four earthquakes and nine performance indices are taken into account to assess the performance of the controllers. To create a good trade-off between the performance and robustness of the closed-loop structural system, a non-dominated sorting genetic algorithm, NSGA-II, is employed. To evaluate the degree of robustness of the controllers, four structural models with uncertainties in the nominal model of the structure is considered. For comparison purposes, a linear quadratic regulator (LQR) controller is also designed in the numerical simulations. Simulation results show that the proposed PD and PID controllers significantly perform better than the LQR in reduction of structural responses. Also, it is shown that the LQR does not provide a good performance in strong earthquakes. However, PD and PID controllers are able to significantly reduce structural responses. Moreover, it is shown that the PID has a better performance than the PD. The results also show that the proposed controllers are capable of maintaining a desired performance in the presence of modeling errors. They also have several advantages over modern controllers in terms of simplicity and reduction of required sensors and computational resources in tall buildings.

Liquefied natural gas (LNG) is a clean burning fossil fuel which offers an energy density comparable to petrol and diesel fuels. There are many commercial processes available for the liquefaction of natural gas, for example single mixed refrigeration, and cascade refrigeration. Another alternative is gas-phase refrigeration processes. These processes are very flexible and inherently safer than condensing refrigeration processes. A shaftwork targeting method has recently been developed for multi-stage gas-phase refrigeration systems (Shah and Hoadley, 2007). In this chapter, a study has been carried out to optimize these systems for multiple objectives, by varying the operating parameters such as the minimum temperature driving force (ΔT_min) and pressure ratio. An interesting aspect of this study is the use of a superstructure model for the process simulation in order to allow for different numbers of refrigeration stages (from 2 to 7 stages). The two objective functions considered in the present optimization study are the capital cost and the energy requirement. A Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to generate the Pareto-optimal front, and an extended range of process parameters including the number of refrigeration stages is tested. The multi-objective optimization results are presented and discussed for two cases: cooling of a nitrogen stream using a nitrogen gas refrigerant, and the dual nitrogen/natural gas refrigerant process for LNG.

Liquefied natural gas (LNG) is a clean burning fossil fuel, which offers an energy density comparable to petrol and diesel fuels. LNG production has grown five-fold in the last 25 years. Although there are a few dominant processes for land-based liquefaction plants, there is much interest in the production of LNG off-shore, particularly “Floating LNG” or FLNG. One option for FLNG is gas phase refrigeration cycles, which are very flexible and inherently safer than condensing refrigeration processes. A shaftwork targeting method has recently been developed for multi-stage gas-phase refrigeration systems (Shah and Hoadley, 2007). In this chapter, a study has been carried out to optimize these systems for multiple objectives, by varying the operating parameters such as the minimum temperature driving force (ΔT_min) and pressure ratio. An interesting aspect of this study is the use of a superstructure model for the process simulation in order to allow for different numbers of refrigeration stages (from 2 to 7 stages). The two objective functions considered in the present optimization study are the capital cost and the energy requirement. A Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to generate the Pareto-optimal front, and an extended range of process parameters including the number of refrigeration stages is tested. The multi-objective optimization results are presented and discussed for two cases: cooling of a nitrogen stream using a nitrogen gas refrigerant, and the dual nitrogen/natural gas refrigerant process for LNG.

This chapter presents three MS Excel programs, namely, EMOO (Excel based Multi-Objective Optimization), NDS (Non-Dominated Sorting) and PM (Performance Metrics) useful for Multi-Objective Optimization (MOO) studies. The EMOO program is for finding non-dominated solutions of a given MOO problem. It has both binary-coded and realcoded NSGA-II (Elitist Non-Dominated Sorting Genetic Algorithm), and two termination criteria based on chi-squared test and steady state detection. The known/true Pareto-optimal front for the application problems is not available unlike that for benchmark problems. Hence, a procedure for obtaining known/true Pareto-optimal front is described in this chapter. The NDS program is for non-dominated sorting and crowding distance calculations of the non-dominated solutions obtained from several optimization runs using same or different MOO programs. The PM program can be used to calculate the values of performance metrics between the non-dominated solutions obtained using a MOO program and the true/known Pareto optimal front. It is useful for comparing the performance of MOO programs to find the non-dominated solutions. Finally, use of EMOO, NDS and PM programs is demonstrated on MOO of amine absorption process for natural gas sweetening.