Multi-objective optimization using MS Excel with an application to design of a falling-film evaporator system

Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm

The parameter estimation tuning is one of the procedures of system identification. One of the identification problems is to find an optimal model structure especially for representing the complex multivariable dynamic systems. Therefore, this problem needs to be solved by using multi-objective optimization with two objective functions namely minimum predictive error and model complexity. In this paper, the tuning of control parameters is studied before comparing multi-objective evolutionary algorithms. The framework is based on the system identification problems. In parameter tuning, two main qualitative parameters of evolutionary algorithms are focused on crossover rate and mutation rate. The multi-objective evolutionary algorithms used for this study are an elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective optimization using differential evolution (MOODE). The different parameter settings are compared in order to obtain good parameter setting for NSGA-II and MOODE. Then, the performance for both algorithms is also compared using Pareto front. The plotted graphs of two objective functions show the non-dominated fronts achieved by the algorithms. The results proved that MOODE shows an advantage in solving system identification problems in a multivariable nonlinear dynamic system.

PurposeThis paper presents a computer‐aided design (CAD) tool for the design of isolated dc‐dc converters.Design/methodology/approachThis tool, developed in Matlab environment, is based on multiobjective optimization (MO) using genetic algorithms. The Elitist Nondominated Sorting Genetic Algorithm is used to perform search and optimization whereas analytical models are used to model the power converters. The design problem requires minimizing the weight, losses and cost of the converter while ensuring the satisfaction of a number of constraints. The optimization variables are, as for them, the operating frequency, the current density, the maximum flux density, the transformer dimensions, the wire diameter, the core material, the conductor material, the converter topology (among Flyback, Forward, Push‐Pull, half‐bridge and full‐bridge topologies), the number of semiconductor devices associated in parallel, the number of cells associated in series or parallel as well as the kinds of input and output connections (serial or parallel) of these cells. Finally, the design of an auxiliary railway power supply is presented and discussed.FindingsThe results show that such tool to design dc‐dc power converters presents several advantages. In particular, it proposes to the designer a set of solutions – instead of a single one – so that he can choose a posteriori which solution best fits the application under consideration. Moreover, interesting solutions not considered a priori can be found with this tool.Originality/valueTo the best of the authors’ knowledge, such a CAD tool including a MO procedure taking several topologies into account has not been suggested so far.

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

Multiobjective optimization (MOO) has been successfully used to improve the process design and operation, by finding trade-offs among conflicting objectives such as energy, capital cost, and profit. In this work, the cumene process design is modified to decrease the raw materials and product losses and to facilitate better energy integration. Here, two slightly different cumene process designs are presented and evaluated. One process design uses a column to vent off the undesired chemicals, whereas the other uses two flash tanks. Additionally, vapor recompression is applied in both the designs to recover energy. Then, MOO of both modified process designs is carried out to examine two trade-offs: total capital cost (TCC) versus material loss and TCC versus utility cost. For this, an Excel-based MOO program is used; it is based on the elitist nondominated sorting genetic algorithm. The cumene process design with column is found to be superior for the first trade-off, whereas the design with two flash tanks is better for the other trade-off. Further, both the designs are compared based on their cumene production capacities; column design is found to be overall superior. Finally, energy requirements of the developed cumene process designs are compared with those reported in recent studies.

Energy storage technology is crucial for promoting the replacement of traditional energy with renewable energy and regulating the energy supply–demand relationship. This paper investigates a triplex-tube thermal energy unit storage to solve the intermediate heat storage and heat transfer problem of hot water supply and demand in clean heating systems. A multi-objective optimization method based on the elitist non-dominated sorting genetic algorithm (NSGA-II) was utilized to optimize the geometric dimensions (inner tube radius r1, casing tube radius r2, and outer tube radius r3), focusing on heat transfer efficiency (ε), heat storage rate (Pt), and mass (M). On this basis, the influence of the optimization variables was analyzed. The optimized configuration (r1=0.014 m, r2=0.041 m, and r3=0.052 m) was integrated into a modular design, achieving a 2.12% improvement in heat transfer efficiency and a 73.23% increase in heat storage rate. Experimental results revealed that higher heat transfer fluid (HTF) temperatures significantly reduce heat storage time, while HTF flow rate has a minimal impact. Increasing the heat release temperature extends the phase change material (PCM) heat release duration, with the flow rate showing negligible effects. The system’s thermal supply capacity is susceptible to heat release temperature.

Heuristic-based multi-objective optimization design for viscous dampers in RC frame structures considering soil-structure interaction

Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm

Ethylene and propylene, building blocks of the petrochemical industries, are mostly produced by steam cracking of hydrocarbons. In our recent work (Nabavi, Rangaiah, Niaei and Salari, Multiobjective Optimization of an Industrial LPG Thermal Cracker using a First Principles Model, Ind. Eng. Chem. Res. 2009, 48, 9523-9533), operation of an industrial liquefied petroleum gas (LPG) cracker was optimized for several sets of two and three objectives. In this work, optimization of an LPG cracker design is investigated for multiple objectives. The objectives considered are maximization of annual ethylene and propylene production, selectivity and run length, and minimization of severity and total heat duty per year. The elitist non-dominated sorting genetic algorithm adapted with the jumping gene operator, NSGA-II-aJG is used to solve the multi-objective optimization problems. The results of design optimization for multiple objectives are compared with those for operation optimization.

In this research, a malfunction diagnosis method based on the enhanced exergy concept is applied to quantify the anomalies' sources in a real integrated solar combined cycle system (ISCCS). A comprehensive parametric study is performed to evaluate the malfunction indicators and the overall performance of the power plant by varying the substantial operating parameters under single and multi-malfunction conditions. Then, the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to maximise the net power and minimise the total exergy destruction rate of the system under a multi-malfunction condition.

An adaptation, inspired by the concept of jumping genes in biology, is developed for the binary-coded elitist nondominated sorting genetic algorithm (NSGA-II). This helps in obtaining global-optimal solutions faster, particularly for problems involving networks. This is because the optimal values of some decision variables in such problems may be 0 or 1, e.g., some streams may be nonexistent in the optimal configuration. It is difficult to generate such chromosomes in the binary-coded NSGA-II (or the unmodified version of the real coded NSGA-II) using the three conventional operations of reproduction, crossover, and mutation. The algorithm developed is used to solve a few sample simple problems involving froth flotation circuits, which represent an important problem in mineral beneficiation. A two-species, two-cell flotation circuit is studied. Both single-objective as well as multi-objective optimizations are performed. The two important objectives used are as follows: (i) the maximization of the recovery of the concentrated ore and (ii) the maximization of the valuable-mineral content (grade) in the concentrated ore. A constraint of a fixed total flotation cell volume is also used. Because these objectives are conflicting, Pareto sets of nondominated solutions are obtained. The algorithm also can be used for the optimization of other networks.

In this paper, separation of a ternary mixture based on simulated moving bed (SMB) technology is studied. The conventional four-zone SMB system for binary separation was modified into different configurations to facilitate simultaneous collection of all three components. The performances of these systems were compared at optimal conditions based on multiobjective optimization for an industrial-scale separation of C8 aromatics. Simultaneous recovery of p-xylene, ethylbenzene, and mixtures of o- and m-xylene with the highest possible purities are considered. The effect of a reflux stream addition containing one of the components is also investigated. It was observed that, as the separation becomes more difficult, the five-zone SMB performs better than the modified four-zone SMB for ternary separation, while at the same time consuming a lesser amount of desorbent. Efficacies in overall performances of the different modified SMB configurations are discussed. Rigorous multiobjective optimization is performed using a nontraditional optimization technique based on an adaptation of a genetic algorithm, elitist nondominated sorting genetic algorithm with jumping genes.

Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA)