Multiobjective optimization of an industrial nylon-6 semi batch reactor using the a-jumping gene adaptations of genetic algorithm and simulated annealing

The elitist nondominated sorting genetic algorithm (NSGA-II) and multiobjective simulated annealing (MOSA) with the robust fixed-length jumping gene adaptation (aJG) are used to solve three computationally intensive multiobjective optimization problems for an industrial semi batch nylon-6 reactor. In Problems 1 and 2, the batch time and the final concentration of the undesirable side-product (cyclic dimer) are minimized while maintaining desired values of the degree of polymerization of the product and the monomer conversion (monomer conversion is maximized as a third objective in Problem 3). The histories of two decision variables, pressure [or vapor release rate] and jacket fluid temperature, are used to obtain the Pareto optimal fronts. The study predicts considerable improvement over earlier results when (i) a single-stage steam jet ejector is used to create subatmospheric pressures in the reactor, (ii) when the jacket fluid temperature is taken as a function of time, and (iii) when some amino caproic acid (from the depolymerization of scrap nylon-6) is added to the feed

Polymerizations in the presence of vaporization: Experimental results on nylon-6

This study deals with the hydrolytic step-growth polymerization of e-caprolactam to produce nylon-6 in a semibatch reactor at near industrial conditions. e-caprolactam is polymerized in a 1.6 L stainless steel reactor at three different initial water concentrations, 4.43% (by mass), 2.52%, and 3.45%, respectively. During the polymerization, the values of the temperature and the pressure are controlled and recorded. Samples of the liquid reaction mass are taken from the reactor at different times and analyzed. The monomer conversions are obtained gravimetrically (in terms of water extractibles) as well as by using gas chromatography. The samples are also analyzed for the degree of polymerization using amide and acid end-group concentrations. The parameters are tuned using one set of data with genetic algorithm. The tuned parameters are then used to predict the second set of data. In the simulation, the poly-NRTL model is used to describe the vapor-liquid equilibria. The simulated values match well with the experimental values. The tuned model gives reasonably good results

Biomimetic adaptations of GA and SA for the robust MO optimization of an industrial nylon-6 reactor

A few jumping gene adaptations have been developed for genetic algorithm and simulated annealing. These are inspired by biology and speed up the convergence. These are used to optimize, multiobjectively, an industrial nylon-6 reactor. Nylon-6 is an important commodity plastic, used for fibers, automobile parts, support for electronics materials, gears for toys, etc. The reaction time (increases the profit) and the final concentration of the undesirable cyclic dimer (improves product quality) are minimized. Robust Pareto solutions are obtained in which a ± 1% variation of the decision variables does not lead to significant deviations in the objective functions

Kinetic modeling and reactor simulation and optimization of industrially important polymerization processes: a perspective

The field of what is now referred to as polymer reaction engineering started in the early 1930s with Staudinger's discovery of macromolecules. Though the earlier work was related primarily to synthesis and kinetics, the field started growing at increasing rates, possibly in the late 1950s or early 1960s. In the early years, this field provided a challenging area of research. It has evolved from the modeling of simple polymerizations to that of more complex systems, to experimentation for filling the gaps in our knowledge, to optimization. This mini-review summarizes a small sampling of the literature in polymerization reaction engineering over the last about four decades using a personal perspective. The concepts in this area are now being applied in a variety of specialized domains, e.g., polymerization at the nano scale, design and control of chain macrostructure and rapid optimal switch-over of grades being manufactured, molecular simulation and computational fluid dynamics, etc

Large-Scale Refinery Crude Oil Scheduling by Integrating Graph Representation and Genetic Algorithm

Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature

Dual-Input Single-Output Isolated Resonant Converter with Zero Voltage Switching

A new modified LCLC series resonant circuit based dual-input single out-put isolated converter is proposed for hybrid energy systems. With this novel converter topology, two different voltage sources can be decoupled completely and transfer the power from two separate dc sources to dc load simultaneously. The proposed converter consists only two controllable switches for integrating two separate voltage sources; it can provide good voltage regulation and soft switching over wide load range. During unequal input voltages, the converter continues to maintain soft-switching and voltage regulation. The proposed converter operation and design considerations are outlined. A laboratory prototype rated for 250 Watt power at an output voltage of 380 V was built-up and tested. Experimental results confirm the functionality of the converter in terms of voltage regulation and soft switching over a wide load range

Optimized on-line control of MMA polymerization using fast multi-objective DE

<p>Optimized on-line control (OOC) of polymerization reactors combine the optimization with the on-line operation and control. In this, re-optimized control variable trajectories, in the presence of unplanned disturbances, are obtained and implemented on-line to save the batch. Also, the available computational time for the optimization is limited as the re-optimized trajectories need to be implemented in real time on the actual system. In the present study, the OOC of such a system, i.e., bulk polymerization of methyl methacrylate (MMA) in a batch reactor, is carried out in the occurrence of heater malfunction. To solve the underlying multi-objective problem, a multi-objective variant of differential evolution with an improved mutation strategy is developed. The developed algorithm shows faster convergence with respect to other compared algorithms for a large number of benchmark problems. Finally, this algorithm is used to find the optimal temperature trajectories and the OOC with these trajectories found to be successfully countering the effect of heater malfunction.</p