Showing papers by "Gade Pandu Rangaiah published in 2002"

A Study of Equation-Solving and Gibbs Free Energy Minimization Methods for Phase Equilibrium Calculations

Abstract: Phase equilibrium calculations are often involved in the design, simulation, and optimization of chemical processes. Reported methods for these calculations are based on either equation-solving or Gibbs free energy minimization approaches. The main objective of this work is to compare selected methods for these two approaches, in terms of reliability to find the correct solution, computational time, and number of K-value evaluations. For this, four equation-solving and three free minimization methods have been selected and applied to commonly encountered vapour–liquid equilibrium (VLE), liquid–liquid equilibrium (LLE), and vapour–liquid–liquid equilibrium (VLLE) examples involving multiple components and popular thermodynamic models. Detailed results show that the equation-solving method based on the Rachford–Rice formulation accompanied by mean value theorem and Wegstein's projection is reliable and efficient for two-phase equilibrium calculations not having local minima. When there are multiple minima and for three-phase equilibrium, the stochastic method, genetic algorithm (GA) followed by modified simplex method of Nelder and Mead (NM) is more reliable and desirable. Generic programs for numerical methods are ineffective for phase equilibrium calculations. These findings are of interest and value to researchers and engineers working on phase equilibrium calculations and/or developing thermodynamic models for phase behaviour.

Simulation and Multiobjective Optimization of an Industrial Hydrogen Plant Based on Refinery Off-Gas

Abstract: A rigorous model is developed for simulating an existing industrial hydrogen plant based on refinery off-gas, which is made up of liquefied petroleum gas and off-gas from the membrane separation unit in a petroleum refinery. The presence of higher hydrocarbons in the reaction system is accounted for in the model equations for bulk gas and catalyst pellet. The reformer model is validated against three sets of industrial plant data, with good agreement. Thereafter, multiobjective optimization is performed using the nondominated sorting genetic algorithm to predict sets of Pareto-optimal operating conditions for improved performance. For a fixed feed rate of off-gas to the unit, two or three objectives, namely, maximization of product hydrogen and export steam rates and minimization of the heat duty supplied to the steam reformer, are targeted simultaneously. The optimal heat flux profile in the steam reformer is distinct from that predicted for methane feed (Oh, P. P.; Ray, A. K.; Rangaiah, G. P. J. Chem. Eng. Jpn. 2001, 34 (11), 1341. The optimal results obtained are better than industrial operating data.

A Methodology for Autotuning of Multivariable Systems

Abstract: A methodology for autotuning decentralized proportional-integral-derivative (PID) controllers for multivariable systems is proposed. It combines the autotuning procedure involving sequential loop closing and relay tests with time-domain curve fitting via least squares to identify suitable transfer function models that account for interactions among the loops, using limited response data. Simulation results reveal that the curve fitting is able to identify models using only the first few oscillations of the relay-test response. Thus, there is no need to wait for the process to reach steady oscillations. A controller tuning method developed by Semino and Scali (J. Process Control 1998, 8 (3), 219-227) is adopted to tune the PI controllers. Detailed results on several 2x2, 3x3, and 4x4 multivariable processes show that the proposed methodology gives PI controllers having performances comparable with those tuned by the ATV+ method of Parabita et al. (Sequential Identification and Autotuning by Relay Techniques of Decentralised Controllers for MIMO Processes, ADCHEM 2000, Pisa, Italy, 2000) or Luyben's BLT technique (Ind. Eng. Chem. Process Des. Dev. 1986, 25 (3), 654-660). The major advantage of the proposed autotuning with least-squares is the significant reduction in the duration of relay tests. Computations involved in the methodology can be carried out on a personal computer in reasonable time.

Optimal design and operation of an industrial hydrogen plant for multiple objectives

Abstract: Many hydrogen plants are currently in operation in chemical and process industries. Increasing demand for hydrogen necessitates optimal additions and changes to these plants. In this work, various retrofitting options for an existing hydrogen plant are considered. For each scenario, optimal design and operating conditions are evaluated to simultaneously maximize hydrogen production and steam generation. First, the heat flux profile in the side-fired reforming furnace is optimized for improved plant performance. Then, two retrofitting cases are considered, namely, debottlenecking waste-heat exchangers and increasing the capacity of the high temperature shift (HTS) and low temperature shift (LTS) converters. Finally, number and length of tubes in the steam reformer are also optimized. Multi-objective optimization was carried out by a non-dominated sorting genetic algorithm (NSGA). When compared to the work of Rajesh et al. who considered only the optimal operation of the same industrial unit, the present optimization results on retrofitting indicate that significant margin exists for improving existing hydrogen plants.