Optimization Of Chemical Processes Research Articles

Kinetic Parameter Estimation Using Modified Differential Evolution

For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computation approach for solving such problems. In this work, a modified version of Differential Evolution (DE) algorithm [named Modified Differential evolution (MDE)] is used to solve a kinetic parameter estimation problem from chemical engineering field. The computational efficiency of MDE is compared with that of original DE and Trigonometric Differential Evolution (TDE). Results indicate that performance of MDE algorithm is better than that of DE and TDE.

Using fuzzy logic to tune an evolutionary algorithm for dynamic optimization of chemical processes

Dynamic optimization of chemical processes can be carried out with evolutionary algorithms that involve many parameters. These parameters need to be given appropriate values for the algorithms to perform efficiently. This paper proposes parameter setting methods based on factorial experimentation and fuzzy logic, aimed at balancing convergence speed, robustness (consistent performance for each problem) and versatility (applicability to many different problems). The methods were tested on an existing dynamic optimisation method with at least nine tuneable parameters. The test problem set turned out to be quite demanding due to one particular problem behaving in opposite direction to the rest with respect to the most influential factor, population size. It is probable that no single tuning would be possible that will satisfy all problems. However, for the other problems, the Fuzzy Logic tuning method proposed in this paper proves to be a very promising approach.

Dynamic Modeling and Nonlinear Predictive Control Based on Partitioned Model and Nonlinear Optimization

The paper presents a combination modeling procedure and the implementation of a nonlinear predictive control scheme for the optimization of industrial chemical processes. The model structure is first based on a simple step response method. This provides a way to use prior knowledge about the dynamics, which has a general validity, while additional information about the process behavior is derived from measured plant data-model error. This data error driven model framework is applicable for a wide range of chemical operating units under a certain control policy. The same idea is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the modeling training algorithm and the nonlinear predictive control approach are demonstrated through a coke furnace case study. A good model fitting for the nonlinear plant is obtained by using the new method. A comparison with traditional approaches shows that the new algorithm can considerably reduce modeling err...

Two-stage optimization problem with chance constraints

In general, chemical processes (CP) are designed with the use of inaccurate mathematical models. Therefore, it is important to create a chemical process that guarantees satisfaction of all design specifications either exactly or with some probability. The paper considers the issue of chemical process optimization when at the operation stage the design specification should be met with some probability and the control variables can be changed. We developed a new formulation of the two-stage optimization problem (TSOP) with chance constraints. On the basis of this formulation and approximate transformation of chance constraints into deterministic ones the iteration method of solving the TSOP with chance constraints is devised.

Bayesian migration of Gaussian process regression for rapid process modeling and optimization

AbstractData-based empirical models, though widely used in process optimization, are restricted to a specific process being modeled. Model migration has been proved to be an effective technique to adapt a base model from a old process to a new but similar process. This paper proposes to apply the flexible Gaussian process regression (GPR) for empirical modeling, and develops a Bayesian method for migrating the GPR model. The migration is conducted by a functional scale-bias correction of the base model, as opposed to the restrictive parametric scale-bias approach. Furthermore, an iterative approach that jointly accomplishes model migration and process optimization is presented. This is in contrast to the conventional “two-step” method whereby an accurate model is developed prior to model-based optimization. A rigorous statistical measure, the expected improvement, is adopted for optimization in the presence of prediction uncertainty. The proposed methodology has been applied to the optimization of a simulated chemical process, and a real catalytic reaction for the epoxidation of trans-stilbene.

Two-step problems of optimization of chemical engineering processes

Two-step problems of optimization of chemical engineering processes

Work flow in process development for energy efficient processes

Increasing expenditures for energy require an optimization of chemical processes with regard to energy efficiency. Energy efficiency is of course only one aspect of a multi-objective optimization during process development. It will be shown how methods for increasing energy efficiency are integrated in the workflow of BASF’s process development for new and existing processes. Special focus will be on the use of exergy analysis and its high relevance to industrial chemical processes. It will be shown how exergy analysis might be used for comparison of different process concepts. Additionally examples will emphasize how an increase of energy efficiency by change of operational conditions can be reached. Also these measures can be interpreted in terms of reduced exergy losses. For all examples the additional investment, if needed, is justified by the savings, which were altogether approximately 7 million euro per year.

Optimization of metal-assisted chemical etching process in fabrication of p-type silicon wire arrays

The metal-assisted chemical etching (MCE) has attracted great attention because of the advantages such as simple and large area process, low-cost and compatible with existing semiconductor technology. We have achieved optimization conditions about two kinds of MCE method for fabrication of p-type silicon wire arrays. First one is one-step MCE that used AgNO 3 and BOE solution and second is two-step MCE in which silicon etching proceeds by BOE and H 2O 2 solution after depositing Au by thermal evaporation. Particularly, the effect of etching parameters, such as etching time and AgNO 3 concentration in one-step MCE and metal catalyst thickness and etching solution temperature in two-step MCE on fabrication of silicon was investigated a morphology, length and etch rate of silicon wire. Finally, we have found important factors for fabrication of uniform and well aligned p-type silicon wire by MCE.

Chromanol derivatives—A novel class of CETP inhibitors

Based on our former development candidate BAY 38-1315, optimization efforts led to the discovery of a novel chemical class of orally active cholesteryl ester transfer protein (CETP) inhibitors. The chromanol derivative 19b is a highly potent CETP inhibitor with favorable pharmacokinetic properties suitable for clinical studies. Chemical process optimization furnished a robust synthesis for a kilogram-scale process.

Optimization of chemical technology processes under probabilistic constraints

In designing technical systems in conditions of partial ambiguity of the initial physical, chemical, and economic information, it is important to determine a construction whose control system would guarantee implementation of all the constraints (precisely or with some probability) despite a change in the internal and external factors during the operating stage. The article is devoted to one of such problems and its solution, namely, the single-stage problem with probabilistic constraints. The approach to solving problems of this kind, based on a transformation of probabilistic constraints into determinate ones, has been suggested.

Inline chemical process analysis in micro-plants based on thermoelectric flow and impedimetric sensors

In micro-plants, as used in chemical micro-process engineering, an integrated inline analytics is regarded as an important factor for the development and optimization of chemical processes. Up to now, there is a lack of sensitive, robust and low-priced micro-sensors for monitoring mixing and chemical conversion in micro-fluidic channels. In this paper a novel sensor system combining an impedimetric sensor and a novel pressure stable thermoelectric flow sensor for monitoring chemical reactions in micro-plants is presented. The CMOS-technology-based impedimetric sensor mainly consists of two capacitively coupled interdigital electrodes on a silicon chip. The thermoelectric flow sensor consists of a heater in between two thermopiles on a perforated membrane. The pulsed and constant current feeds of the heater were analyzed. Both sensors enable the analysis of chemical conversion by means of changes in the thermal and electrical properties of the liquid. The homogeneously catalyzed synthesis of n-butyl acetate as a chemical model system was studied. Experimental results revealed that in an overpressure regime, relative changes of less than 1% in terms of thermal and electrical properties can be detected. Furthermore, the transition from one to two liquid phases accompanied by the change in slug flow conditions could be reproducibly detected.

Dynamic Optimization of Chemical and Bio-chemical Processes using an Efficient Global Optimization Metaheuristic

Dynamic Optimization of Chemical and Bio-chemical Processes using an Efficient Global Optimization Metaheuristic

Initial guess of the solution of dynamic optimization of chemical processes

Numerical methods and software packages for solving dynamic optimi- zation or optimal control problems require a suitable initial estimation of the solution. This paper focuses on problems that arise in chemical processes described by com- plex dynamics. We present a very simple method, based on Pontryagin's Minimum Principle, to obtain an initial guess for the solution. Our method presents numerous advantages: it is very easy to programme, it allows a wide range of problems to be addressed, the computation time is very short, the initial guess is very close to the solution and is attracted to a global minimum.

One-stage optimization problem with chance constraints

Generally, the design of chemical processes (CP) is performed with the use of inaccurate mathematical models. Therefore, it is essential to create chemical processes that can satisfy all the design specifications at the operation stage in spite of the changes in internal and external factors. Consequently, the problem of chemical process optimization under uncertainty is of prime importance in chemical engineering. The paper considers one-stage optimization problems with chance constraints. The main issue in solving one-stage optimization problems is calculation of multiple integrals (calculating the expected value of the objective function and probabilities of constraints satisfaction). Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones. A computational experiment has shown the efficiency of this approach.

Finite‐Time Thermodynamics and the Optimal Control of Chemical Syntheses

AbstractThe optimization of chemical processes that take place in a finite time constitutes an important application of finite‐time thermodynamics. In this study we investigate two generic optimal control problems for nucleation‐and‐growth based syntheses: the maximization of the amount of a crystalline solid phase generated via cooling from the melt within a finite time τ, and the maximization of the difference between two metastable crystalline modifications again synthesized by crystallization from a supercooled melt. In both cases the optimal temperature program consists in a bang‐bang solution with constant values of the temperature, where a switch from a temperature T1, where nucleation rates are high, to a temperature T0 > T1, where the growth rates of the crystallites are maximal, occurs. The location of the switching time ts* (0 ≤ ts* ≤ τ) is analyzed as function of the parameters of the models describing the chemical systems, and an application to the synthesis of glycerol crystals is given.

Simulation of (bio)chemical processes with distributed parameters using Matlab®

Nowadays, simulations have become indispensable for the analysis and optimisation of (bio)chemical processes. However, as a lot of these processes are distributed in nature (i.e., the properties vary both in time and space), their simulation requires the solution of non-linear convection–reaction–diffusion partial differential equations (PDEs). Therefore, this paper compares different solution methods in a comprehensible way in order to provide practical guidelines. Moreover, to stimulate their usage in practice, all techniques have been implemented in Matlab ®, and all test examples have been made available ( www.matmol.org). This allows practitioners to rapidly evaluate different methods when setting-up their own process simulation code. Finally, a complex reverse flow reactor case illustrates how these methods can be successfully combined with optimisation approaches.

Editorial: Special Issue of 8th World Congress of Chemical Engineering (WCCE 8) Symposium on Process Design

The 8th World Congress of Chemical Engineering (WCCE 8) Symposium on Process Design, held on August 23-27, 2009 in Montreal, Canada, offered a unique opportunity to focus on a comprehensive participation and learning experience over the full range and diversity of methods in the broad fields of process modeling and their use for design, simulation, optimization and control of chemical processes. The major themes of the symposium are categorized into the following themes of the congress:• Energy • Green Processing • New Materials • BiotechnologyAuthors of selected papers accepted for presentation in the Process Design symposium were invited to submit the full papers to be considered for publication in the journal of Chemical Product and Process Modeling (http://www.bepress.com/cppm). The papers that successfully passed the reviewing process of the journal are now published in the WCCE8 special issue of the journal.

Metodología para la Obtención de Modelos Semifísicos de Base Fenomenológica Aplicada a una Sulfitadora de Jugo de Caña de Azúcar

This work presents the Phenomenological Based Semi-physical Model (PBSM) as a useful tool for the design, control and optimization of chemical and bio-technology processes. First, a detailed description of a methodology to obtain a PBSM based on the knowledge of the basic principles of the process: mass, energy and momentum conservation, as well as gradient principle for obtaining model constitutive equations. The described methodology is applied to the clarification stage of a sugar cane refining plant. Specifically, the sulfitation tower is modelled step by step. The model is simulated and validated by comparing its behaviour with data taken from a real sulfitation tower.

Automatic design of conventional distillation column sequence by genetic algorithm

AbstractSynthesis of the optimum conventional (with non‐sharp separations) distillation column sequence (DCS) is a challenging problem, in the field of chemical process design and optimization, due to its huge search space and combinatorial nature. In this paper, a novel procedure for the synthesis of optimum Conventional Distillation Column Sequence is proposed. The proposed method is based on evolutionary algorithms. The main criterion used to screen alternative DCS's is the Total Annual Cost (TAC). In order to estimate the TAC of each DCS alternative all columns that exist in the DCS are designed using short‐cut methods. The performance of the proposed method and other alternatives are compared based on the results obtained for four standard benchmark problems used by researchers working in this area. Based on the results of the comparison, the proposed method outperforms the other methods and is also more flexible than other existing methods.

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

AbstractThis article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.