Romualdo Salcedo

A simulated annealing approach to the solution of minlp problems

An algorithm (M-SIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems is presented. A recently proposed continuous non-linear solver (SIMPSA) is used to update the continuous parameters, and the Metropolis algorithm is used to update the complete solution vector of decision variables. The M-SIMPSA algorithm, which does not require feasible initial points or any problem decomposition, was tested with several functions published in the literature, and results were compared with those obtained with a robust adaptive random search method. For ill-conditioned problems, the proposed approach is shown to be more reliable and more efficient as regards the overcoming of difficulties associated with local optima and in the ability to reach feasibility. The results obtained reveal its adequacy for the optimization of MINLP problems encountered in chemical engineering practice.

Optimization of reactive distillation processes with simulated annealing

A simulated annealing-based algorithm (MSIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems was applied to the synthesis of a non-equilibrium reactive distillation column. A simulation model based on an extension of conventional distillation is proposed for the simulation step of the optimization problem. In the case of ideal vapor}liquid equilibrium, the simulation results are similar to those obtained by Ciric and Gu (1994, AIChE Journal, 40(9), 1479) using the GAMS environment and to those obtained with the AspenPlus modular simulator. The optimization results are also similar to those previously reported and similar to those using an adaptive random search algorithm (MSGA). The optimizations were also performed with non-ideal vapor}liquid equilibrium, considering either distributed feed and reaction trays or single feed and reaction tray. The results show that the optimized objective function values are very similar, and mostly independent of the number of trays and of the reaction distribution. It is shown that the proposed simulation/optimization equation-oriented environments are capable of providing optimized solutions which are close to the global optimum, and reveal its adequacy for the optimization of reactive distillation problems encountered in chemical engineering practice. ( 2000 Elsevier Science Ltd. All rights reserved.

Time-series analysis of air pollution data

Abstract Time-series analysis of air pollution environmental levels involves the identification of long-term variation in the mean (trend) and of cyclical or periodic components. A model based on a stepwise approach to time-series analysis was applied to the daily average concentrations of strong acidity (SA) and black smoke (BS) in the Oporto area, using an available computer program. Each step is completed by a correlation analysis of the residuals, allowing the identification of an optimal structure with a residual white noise. A periodic component with harmonics defined through “peaks” of concentration on week middle days and “troughs” on weekends was observed. SA concentration behaviour can be related with industrial activities, mainly through fossil-fuel burning in discontinuous working cycles. The observed evolution for BS is most probably related with weekly patterns of motor traffic, with observed minimum values during weekends. The periodic components represent, on the average, about 5% of the total variance for the SA series and 15% for the BS series. However, the weekly cycles are predominant in the SA series, representing on the average 75% of the periodic variance, against 46% for the BS series. Statistically significant higher frequency (≈2–4 day) periodic components were observed for both pollutant indicators and for all collection sites analysed. This may be due to synoptic weather variations of minimum and maximum daily temperature and precipitation, which show similar periods in the study area.

AN IMPROVED RANDOM-SEARCH ALGORITHM FOR NON-LINEAR OPTIMIZATION

Abstract An adaptive random search optimization algorithm is presented, which is found to be very efficient in dealing with non-linear constrained and unconstrained problems. The major differences relatively to previously reported algorithms are that variable, parameter dependent compression vectors are employed in the contraction of the search regions and that shifting strategies incorporating “wrong-way” moves are enforced. The algorithm was tested with 24 severe functions published in the literature. Results were compared with those obtained employing another random search method and with published work. The proposed algorithm is shown to be more robust and more efficient in what concerns the overcoming of difficulties associated with local optima, the sensitivity to search intervals and associated compression factors, the starting solution vector and the dependency upon the random number sequence. The results obtained reveal the adequacy of the algorithm for the optimization of a broad range of problems encountered in chemical engineering practice.

GLOBAL OPTIMIZATION OF REVERSE-FLOW GAS CYCLONES: APPLICATION TO SMALL-SCALE CYCLONE DESIGN

This paper addresses the optimum design of reverse-flow gas cyclones through the solution of 2 numerical nonlinear optimization problems that respectively maximize cyclone collection and an efficiency/cost ratio. The simulation model was based on the predictive properties of the 1998 finite diffusivity model of Mothes and Löffler, in which the particle turbulent dispersion coefficient is estimated through an empirical correlation between the radial Peclet and Reynolds numbers. The optimizations were formulated with constraints on pressure drop, saltation velocity, and geometrical considerations such that feasible cyclones could always be obtained. The two geometries, named RS_VHE and RS_K, are different from available high-efficiency designs and represent reverse-flow cyclones with a predicted, significantly improved performance. The geometry RS_VHE was built and tested on a laboratory scale, providing experimental evidence of a significantly improved design.

A new approach to the kinetic modeling of the reaction of gaseous HCl with solid lime at low temperatures

Abstract The chemical reaction between gaseous HCl and solid Ca(OH) 2 has several applications, namely in the dry scrubbing of acid gases. The behavior of this reacting system in a laboratory-scale tubular reactor was simulated using the ‘grain model’ for the solid phase and a cascade of CSTRs for the gaseous phase. This model was applied to experimental data obtained in a previous study, at low temperatures (50–130°C) and high humidity level. The results show that the diffusion of the gaseous reactant through the product solid layer is the rate-limiting step for this reaction. In the first reacting moments, when no solid product has yet been formed, the reaction is limited by the total consumption of the gaseous reactant, indicating a very fast chemical reaction (k s >10 −3 ms −1 ) . These results were compared with those obtained by two other models used to describe the behavior of the gaseous phase: the ‘differential reactor model’ and the ‘average concentration model’. We show that the differential reactor model, which has been largely applied in published works about this and other similar reacting systems, is not adequate for modeling the reaction process, particularly in its initial phase.

Databank transfer-of-information, shortcut and exact estimators used in the wastewater biological treatment process identification

Abstract The biological treatment is the most complex step in removing organic and inorganic pollutants from wastewaters, being very sensitive to input-flow oscillations, operating conditions, and biomass evolution. Sudden increases in substrate concentration or some inhibitory substances, deterioration of the biomass, or few observed species, all of these lead to a difficult process modelling and need replaced biokinetics identification for each waste and biomass type. However, the bioprocess numerical analysis is crucial for obtaining significant improvements in the wastewater treatment (WWT) plant performances and safety indices even under imperfect data. The paper exemplifies an advanced route to quickly on-line identify the biodegradation characteristics of new substrates processed by a series of perfectly mixed aeration basins with biomass recycle. The Monod kinetics is recursively identified by using the available collection of plant previous transient operating data and a robust shortcut estimator (MIP). The approximate solution is periodically refined with an exact nonlinear estimator (NLS), checked for consistency and significance versus prior information, and stored in databanks.

Can Spreadsheet Solvers Solve Demanding Optimization Problems

Practicing engineers resort to modular simulators or to algebraic tools such as GAMS or AMPL for performing complex process optimizations. These tools, however, have a significant learning curve unless they have been introduced at the undergraduate level beforehand. In this work we show how the Solver feature of the Excel spreadsheet can be used for the optimization of a fairly complex system, i.e., a classic solvent extraction/pollution prevention with heat integration process. The specific goal was the design optimization for continuous recovery of organic solvents (VOCs) using a gas absorption tower with solvent recovery in a stripper. It is shown that the Solver feature of the Excel spreadsheet can be used to converge on local optima for these complex systems, provided proper care is taken in the solution procedure. The complexities of optimization can also be demonstrated with this tool, as can several common pitfalls encountered during optimization.

An Optimized Strategy for Equation-Oriented Global Optimization

Abstract An optimized strategy for simulation and optimization of steady-state processes, under an equation-oriented environment, is presented. Equation-oriented environments apply a solution procedure to solve the entire system of non-linear algebraic equations arising from the mathematical model describing these processes. The difficulty in solving these systems may change drastically by specifying different independent variables for the degrees of freedom, as it has long being recognized. An algorithm that chooses the decision variables by minimizing the number and size of the subsystems of equations that need to be simultaneously solved for, while allowing for the inclusion of functional constraints, is used (Salcedo and Lima, 1999). With this algorithm, optimum sets of decision variables and the corresponding solution strategies are obtained. This paper describes the implementation of this approach linked with a simulated annealing-based global optimizer. The proposed strategy was applied to the optimization of a reactor-extractor system and to a more difficult absorber-stripping system with heat integration. With these examples we pretend to compare different optimization procedures for each test case, respectively solving the entire system of equations, solving some smaller subsystems (a local optimum for the simulation step) or solving for the global optimum of the simulation step (which may correspond to a sequential solution). By optimizing the simulation step much more accurate results as well as significantly reduced CPU times are obtained, in comparison with simultaneous solution strategies, suggesting that this may be a powerful tool for global optimization.

Pharmaceuticals: Efficient cyclone systems for fine particle collection

Numerically optimised cyclones and recirculation systems have been shown to be highly efficient for the capture of fine particles, such as those occurring in the production of active pharmaceutical and food ingredients. Romualdo Salcedo and Julio Paiva describe some of the recent developments in this area.