Multi-objective Optimization of Industrial Styrene Production Using a Process Simulator and a Genetic Algorithm

Optimization of the whole plant instead of important individual units is essential for maximizing savings and operational efficiency. Often, there are conflicting objectives for optimizing industrial processes. Many previous studies on multi-objective optimization involved a few critical units (and not complete plants) using models and simulation programs specifically developed for the respective application. Developing rigorous models and a separate code for simulating a complete plant, for the sake of multi-objective optimization is difficult and time consuming. There is potential to make this task easier by employing available process simulators such as Aspen Plus and Hysys. But these simulators do not currently have multi-objective optimization tools. Hence, an interface has been developed between Non-dominated Sorted Genetic Algorithm (NSGA-II) and Hysys. Plant-wide optimization using this interface involves three main steps: (a) development and testing of Hysys model for steady simulation of the process under study; (b) sensitivity analysis and selection of objectives, decision variables and constraints; and (c) optimization of the process for multiple objectives using NSGA-II. This paper describes optimization of a styrene unit/plant for multiple objectives using the interface and compares the obtained results with those obtained using an independently developed simulation program. Key works: Plant-wide optimization, multi-objective optimization, genetic algorithms, NSGA-II, Hysys Introduction Multi-objective problems are important to operate a plant/reactor in an optimized way to have good productivity, yield and/or selectivity with minimal utilization of resources, waste formation and/or pollution. To achieve these goals, optimal operating conditions need to be identified. What is even more important is to formulate and solve an optimization problem based on plant-wide perspective. With the availability of effective methods for multi-objective optimization [2], several studies on multi-objective optimization of important industrial processes and reactors like nylon 6 [6], wiped-film poly (ethylene terephthalate) reactor [1], hydrogen plant [7], epoxy polymerization process [3] and aspergillus niger fermentations for selective product enhancements [5] have been reported. Multi-objective optimization of industrial styrene reactors using non-dominated sorting genetic algorithm (NSGA-II) was performed by Yee et al. [8]. Twoand three-objectives, namely, production, yield and selectivity of styrene, were considered for adiabatic and steam-injected styrene reactors. Pareto-optimal solutions were obtained due to conflicting effect of either ethyl benzene feed temperature or flow rate. Different variants of NSGA-II were tested for multiobjective optimization of a styrene reactor [11]. The work of [8] was extended to optimizing an industrial styrene manufacture [12]. Practically all the above studies have been performed by writing a simulation program for the reactor/plant in F90 or C++ followed by optimization for multiple objectives. The simulation program was often based on simplified models for units like heat exchanger, partial condenser and distillation column. Obviously developing the simulation program for a whole plant having many units and recycles is time consuming and needs a lot of effort. Also, optimization requires additional effort due to lack of interactive environment. To overcome these problems, an interface has been developed in our research laboratory between NSGA-II in C++ for multi-objective optimization and Hysys that provides a user-friendly environment for process flowsheet development and sensitivity analysis. Development of this interface along with some useful pointers is presented in [13]. This paper briefly describes the using/working of the interface followed by the successful multi-objective optimization of an industrial styrene production unit/plant using Hysys via the interface. Results obtained from the multi-objective optimization by two ways: (1) using F90 code with NSGA-II and (2) using the interface between Hysys and NSGA-II, are compared. The latter not only captures the features and powers of simulators for simulating industrial processes but also makes effective use of genetic algorithms for multiobjective optimization. It also facilitates the employment of new optimization techniques. Design and operating data for an industrial styrene reactor from Elnashaie and Elshishini [4] formed the basis for the complete plant. The results of multi-objective optimization provide an extensive range of optimal operating conditions, from which a suitable operating point can be selected based on the specific requirements in the plant. Using/Working of the Interface The interface facilitates the multi-objective optimization of Hysys simulation using NSGA, and involves a number of steps (sketch below). The user supplies the genetic algorithm parameters like crossover & mutation probability, seed for random number generation, population size and maximum number of generations as well as number of (binary/real) decision variables, constraints and objectives through the user interface. When the application is run, these data are used by the optimizer to start simulation followed by initialization of the population of points in the decision variable space by NSGA. The initial population is stored as an array and passed to the visual basic application (VBA), which makes a call to Hysys and supplies decision variables set one by one through the built-in spreadsheet of Hysys. The flowsheet is simulated for the supplied decision variables and the user defined objectives are evaluated in the spreadsheet itself. These objective values are later stored in an array in VBA and ultimately passed to NSGA where individuals are ranked according to there fitness values. After selection, mutation, and crossover operations, next population of points is chosen and submitted to Hysys for simulation and computation of objectives. This carries on till the maximum number of generations is reached. Figure AWorking procedure for Interface. Modeling and Simulation of the Styrene Reactor Unit Modeling, simulation and optimization of an industrial styrene reactor unit/plant has been performed by Tarafder et al. [12] using the corrected kinetic model of Sheel and Crowe Visual Basic Application No. of Decision variables No. of Objectives No. of Constraints GA Parameters 8 2 5 Run Optimization Plant Model (HYSYS) Optimizer (Genetic Algorithm) START Objectives DVs Constraint Constraint DVs GA Parameters Objectives [4, 10]. Similar to the study of Tarafder et al. [12], the styrene reactor unit used as Case 1 in the present study, consists of plug flow styrene monomer reactor along with heat-exchanger (HE1) and the superheater for steam (Fig. 1). The overall plant includes all these units and heat exchanger (HE2), partial condenser (PC) and three distillation columns (S1, S2 and S3) as shown in Fig. 1. The model details (mass energy and momentum balance equations), assumptions, rate kinetics data, catalyst information, the six main reactions, including side reactions, occurring in the styrene reactor and operating data for simulation and validation are available in [4, 8]. The same model parameters were used to simulate the flowsheet in Hysys before interfacing with NSGA for optimization. The simulation results obtained using Hysys and F90 program of [12] are very close (Table 1) and the minor differences are due to differences in physical properties. The predicted results are also comparable to the industrial data (Table 1). Figure 1: Schematic diagram of Styrene plant. (HE: heat-exchanger, S: Separator, PC: partial condenser). Letters in bold & italics represent decision variables [12] HE1 HE2 PC Reactor Steam superheater Benzene-toluene Splitter EB recycle Column Benzene