An existing side-fired steam reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, “optimal” conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.

Process optimization often has two or more objectives which are conflicting. For such situations, multiobjective optimization (MOO) provides many optimal solutions, which are equally good from the perspective of the given objectives. These solutions, known as Paretooptimal front and as nondominated solutions, provide deeper insights into the trade-off among the objectives and many choices for implementation. In the past 20 years, researchers have applied MOO to numerous applications in chemical engineering. However, selection of an optimal solution from the set of nondominated solutions has not received much attention in the chemical engineering literature. In the present study, 10 methods for selecting an optimal solution from the Pareto-optimal front are carefully chosen and implemented in an MS Excel-based program. Then, they are applied to the selection of an optimal solution in many benchmark or mathematical problems and chemical engineering applications, and their effectiveness and similarities are analyzed. Results of analysis indicate that, among the 10 methods studied, technique for order of preference by similarity to ideal solution, gray relational analysis, and simple additive weighting are better for choosing one of the Pareto-optimal solutions.

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The application of semiconductors in water treatment via photocatalysis of various pollutants has attracted much attention from researchers. In this work, photocatalytic degradation of methylene blue by P25 titanium dioxide was studied experimentally and then via modeling. The effects of lamp choice, concentration of catalyst, and methylene blue were analyzed. Desorption of methylene blue at the start of light radiation was observed, and analyzed in detail for the first time. Both desorption and degradation processes were modeled, and experimental data was fitted to a pseudo-first-order model with sufficient accuracy. The effects of catalyst and initial dye concentration on reaction rate constants were discussed.

Plantwide control (PWC) methodologies have gained significant importance given the high and increasing degree of integration in chemical processes due to material recycle, energy integration, and stringent product quality control, all of which though economically favorable, pose tough challenges to smooth plant operation. As part of the continuing search for more effective PWC system design methods, an integrated framework of heuristics and simulation was proposed [Konda et al. Ind. Eng. Chem. Res. 2005, 44, 8300-8313]. The basic idea behind this development is to make effective use of rigorous process simulators to aid in decisionmaking during the development of the heuristic-based PWC structure. Konda and co-workers have successfully applied the procedure to the toluene hydrodealkylation process. Though the integrated framework is promising, there is still a need to test its applicability to other complex industrial processes. The present contribution considers the development of a PWC structure for the styrene monomer plant using the integrated framework. In addition, in order to gauge its effectiveness in comparison to the other plantwide control methods, two more methods are considered in this study. First, the heuristics procedure of Luyben and co-workers [Luyben et al. Plant-Wide Process Control; McGraw-Hill: New York, 1998], which is a popular heuristics-based methodology, is also applied to the same flowsheet, and is considered as the base case for performance assessment. Second, the self-optimizing control procedure [Skogestad, S. Comput. Chem. Eng. 2004, 28, 219-234] is also used in order to have a more comprehensive analysis of the effectiveness of the integrated framework. An analysis of the results indicates that while all the procedures give stable control structures, the integrated framework and self-optimizing control procedures give more robust control structures than the heuristics procedure. This is the first study to develop simulation models and complete PWC structures for the styrene plant, together with a detailed analysis of the relative performance of the resulting structures in order to evaluate the different PWC methodologies.

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The first-principles, data-based, and hybrid modeling strategies are employed to simulate an industrial hydrocracking unit, to make a comparative performance assessment of these strategies, and to do optimization. A first-principles model (FPM) based on the pseudocomponent approach (Bhutani, N.; Ray, A. K.; Rangaiah, G. P. Ind. Eng. Chem. Res. 2006, 45, 1354 is coupled with neural network(s) in different hybrid architectures. Data-based and hybrid models are promising for important predictions in the presence of variations in operating conditions, feed quality, and catalyst deactivation. Data-based models are purely empirical and are developed using neural networks, whereas the neural-network component of a hybrid model is used to obtain either updated model parameters in the FPM connected in series or to correct predictions of the FPM. This article presents data-based models and three hybrid models, their implementation and evaluation on an industrial hydrocracking unit for predicting steady-state performance, and finally the optimization of the hydrocracking unit using the data-based model and a genetic algorithm.