Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm (original) (raw)

Systematic Framework for Multiobjective Optimization in Chemical Process Plant Design

Advances in Chemical Engineering, 2012

 Generation of alternatives and problem definition;  Analysis of alternatives i.e. generation of relevant data for comparison of Environmental, economic and safety objectives  Multiobjective decision analysis/ optimization  Design evaluation stage i.e. decision making from the pareto-surface of non-inferior solution or ranking of alternatives www.intechopen.com

Multi-criteria Optimization of an Industrial World-Scale Process

Chemie Ingenieur Technik, 2017

Gü nter Wozny on the occasion of his 70th birthday Commercially available flow sheet simulators cannot perform optimization runs with more than one criterion. To overcome this problem, the flow sheet simulator CHEMCAD is combined with an external optimization solver, and the Excel VBA client is used to arrange inter-process communications. The resulting tool for multi-criteria optimization in chemical process engineering is applied to two separation problems. First, a dividing-wall column is used as a theoretical example to test different starting points and different scalarization techniques. Second, a significant decrease in energy demand is achieved for a real continuous world-scale facility.

Multiobjective Optimization in Terms of Economics and Potential Environment Impact for Process Design and Analysis in a Chemical Process Simulator

Industrial & Engineering Chemistry Research, 1999

This paper is devoted to an application of the MOOP (multiobjective optimization programming) concept to the practical field of chemical engineering to take into account the trade-off between economics and pollution with appropriate analysis methods. Optimization of the process is performed along an infeasible path with the SQP (successive quadratic programming) algorithm. One of the objective functions, the global pollution index function, is based on potential environmental impact indexes calculated by using the hazard value (HV). The other is the costbenefit function. To analyze the biobjective optimization system in terms of economics and potential environmental impact, the noninferior solution curve (Pareto curve) is formed using SWOF (summation of weighted objective functions), GP (goal programming), and PSI (parameter space investigation) methods within a chemical process simulator. We can find the ideal compromise solution set based on the Pareto curve. The multiobjective problem is then interpreted by sensitivity and elasticity analyses of the Pareto curve that give the decision basis between the conflicting objectives.

Optimization and Decision Making in Chemical Engineering Problems

The applications of Multiple Criteria Decision Making (MCDM) in dealing with the chemical engineering optimization problems are rapidly increasing. It has been inspired by increased computational resources and the effectiveness of the methods for solving the Multiple Objective Optimizations (MOO). Meanwhile the number of objectives in MOO of chemical applications, due to the inclusion of the new economical and environmental objectives to the processes, is increasing. As a result, the most recent utilized MOO methods cannot effectively deal with this expansion. However it is important that when selecting a method, the pros and cons set by the method are understood. Otherwise, the optimal results may not deliver the true impression about the problem. In this situation this paper aims to widen the awareness of the readers of the existence of interactive methods, in particular the NIMBUS method, which are capable of handling MOO problems with more than two objectives. For this reason some encouraging experiences and advantages of the NIMBUS method in recent chemical engineering applications are briefly reviewed following a brief introduction to the whole subject.

Environmentally conscious design of chemical processes and products: Multi-optimization method

Chemical Engineering Research and Design, 2009

This paper presents an environmentally conscious integrated methodology for design and optimization of chemical process especially for separation process, whose energy consumption occupies more than 70% of the whole process. The methodology incorporates environmental factors into the chemical process synthesis at the initial design stage, which is totally different with the traditional end-of-pipe treatment method. Firstly, one rigorous model for simulation of multi-stages and multi-components separation process was developed, and based on our proposed environmental impact assessment method, the calculation methods of the reasonable economic and environment objective are constructed. Then one multi-objective mixed integer non-linear mathematical model was established by considering environmental and economic factors. Finally, the high non-linear model was solved by multi-objective evolutionary algorithm (non-dominated sorting genetic algorithm). It is often difficult to find an optimum for a process that satisfies both economic and environmental objectives simultaneously. Normally, an arrangement of optimal solutions is obtained, which forms a non-inferior set. Identifying the optimum from this non-inferior set is subjective, depending on the preference of decision makers. In this paper, technique for order preference by similarity to ideal solution (TOPSIS) for identifying the set of optimal parameters is developed and used at the decision-making step, in which the preference relation for the decision-maker over the objectives is adopted by trade-off information between objectives. The proposed methodology was highlighted through two industrialized processes, dimethyl carbonate production processes by pressure-swing distillation and extraction distillation process, respectively.

A structural optimization approach in process synthesis—I

Computers & Chemical Engineering, 1983

mixed-integer linear programming approach is presented for performing structural and parameter optimization in the synthesis of processing systems. This approach is applied to the synthesis of utility systems that have to provide fixed demands of electricity, power for drivers and steam at various pressure levels. A superstructure that has embedded many potential configurations of utility systems is proposed, as well as its corresponding mixed-integer programming model. The application of the model is illustrated with a large example problem.

A mixed-integer nonlinear programming algorithm for process systems synthesis

AIChE Journal, 1986

The problem of synthesizing processing systems via simultaneous structural and parameter optimization is addressed in this paper. Based on a superstructure representation for embedding alternative configurations, a general mixed-integer nonlinear programming (MINLP) framework is presented for the synthesis problem. An efficient outerapproximation algorithm is described for the solution of the underlying optimization problem, which is characterized by linear binary variables and continuous variables that appear in nonlinear functions. The proposed algorithm is based on a bounding sequence that requires the analysis of few system configurations, and the solution of a master problem that identifies new candidate structures. Application of the proposed algorithm is illustrated with the optimal synthesis of gas pipelines. Correspondence concerning this paper should be addressed to 1. E. Grossmann. M. A. Duran's currenl address is Universidad Autonoma Metropolitans-lztapalapa. A*. Postal 55-534.09340 MexicoCity.

Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

Computers & Industrial Engineering, 2008

This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems.

Applications of Multiobjective Optimization In Chemical Engineering

Reviews in Chemical Engineering, 2000

V. Bhaskar, 1 Santosh K. Gupta 2 and Ajay K. Ray 1 * ... 1 Department of Chemical and Environmental Engineering National University of Singapore 10, Kent Ridge Crescent Singapore 119260, SINGAPORE ... 2 Department of Chemical Engineering University of Wisconsin Madison, WI ...