Optimization Of Chemical Processes Research Articles

Conceptual design and optimization of chemical processes under uncertainty by two-stage programming

This contribution presents a method and a tool for modelling and optimizing process superstructures in the early phase of process design where the models of the processing units and other data are inaccurate. To adequately deal with this uncertainty, we employ a two-stage formulation where the operational parameters can be adapted to the realization of the uncertainty while the design parameters are the first-stage decisions. The uncertainty is represented by a set of discrete scenarios and the optimization problem is solved by stage decomposition. The approach is implemented in the computer tool FSOpt (Flow sheet Superstructure Optimization) FSOpt provides a flexible environment for the modelling of the unit operations and the generation of superstructures and algorithms for the translation of the superstructure into non-linear programming models.The approach is applied to two case studies, the hydroformylation of dodec-1-ene and the separation of an azeotropic mixture of water and formic acid.

Optimization of Chemical Mechanical Planarization Process of High Enrichment Slurry Under Low Pressure

The polishing process was optimized according to the polishing rate and its consistency of HE slurry with different dilution multiple on the copper wafers, it can be confirmed that: the best pressure value of HE1, HE10, HE20, HE50 type slurries was 6890Pa, the best flow rate value of the preceding three slurries was 300ml/min, the best value of the HE50 slurry was 400ml/min. Through the planarization effects of the slurries with different dilution multiple, it can be obtained that: the initial dishing and erosion heights of the samples were both 1270nm and -500nm, and the two values respectively changed to 539.3nm, -75.7nm and 796.3nm, -191.3nm after being treated by HE1 and HE10 slurries, the step height of the wafer changed from 117nm to 72nm after being treated by HE20 slurry, the step height of the wafer changed from 88nm to 71nm after being treated by HE50 slurry. It was concluded that: the HE slurry shows strong ability for step removal when the slurry is diluted by 1 times and 10 times, the HE slurry also owns high planarization ability when the slurry is diluted by 20 times and 50 times.

Steady-state optimization of chemical processes with guaranteed robust stability and controllability under parametric uncertainty and disturbances

A new methodology is proposed for the steady-state optimal design of chemical processes under parametric uncertainty and disturbances. The methodology allows the integration of uniform constraints for robust controllability and stability in an optimization problem by using the Routh–Hurwitz test and zero dynamics based method. The underlying mathematical problem is difficult to solve because it involves infinite stability and controllability constraints. We developed an algorithm where an infinite number of constraints can be implemented as several relaxation problems that are solved iteratively. Additionally, the dynamic simulation results under parametric uncertainty and disturbances are also used to estimate the bound of the state perturbations rather than assumptions based on experience, which may lead to overly conservative or non-implementable design. To illustrate the methodology, three different examples are presented and robustly stable and controllable designs are obtained.

Mathematical modeling of chemical reactions in microreactors

In this paper it is conducted a review of published data on the use of microstructural reactors for the optimization of chemical processes and reactions, a detailed description of which is possible with the use of modern methods and modeling tools. Microreactor is a miniature reactor apparatus having dimensions in the submillimeter range. Chemical reactions in the microreactor are carried out in one or a plurality of parallel channels, the channel diameter is not more than the mean free path of the molecules in the gas phase reactions. The advantages of the microreactors may include a large surface area contact between the phases, laminar flow (and reducing energy costs and increasing residence time), high radial diffusion coefficient, simplifying scaling and enhanced characteristics of safety selectivity of processes occurring in them. On the basis of established technical and economic advantages of microsystem devices it is proved the feasibility of their implementation in production.

Optimization of Chemical Process Design with Chance Constraints by an Iterative Partitioning Approach

Generally, chemical processes are designed with the use of inaccurate mathematical models. Therefore, it is important to create a chemical process that guarantees the satisfaction of all design specifications either exactly or with some probability. This paper considers the issue of chemical process optimization when at the operation stage the design specifications should be met with some probability and the control variables can be changed. We have developed a common approach for solving one-stage and two-stage optimization problems with joint chance constraints for the cases in which the uncertain parameters are either independent random variables with arbitrary probability distributions or dependent normally distributed random variables. This approach is based on approximate transformation of chance constraints into deterministic constraints. This excludes the numerical calculation of multiple integrals for computation of the left-hand sides of chance constraints.

H2Mapping on Pt-Loaded TiO2Nanotube Gradient Arrays

We describe a rapid screening technique for determining the optimal characteristics of nanophotocatalysts for the production of H2 on a single surface. Arrays of TiO2 nanotubes (NTs) with a gradient in length and diameter were fabricated by bipolar anodization, and a perpendicular gradient of Pt nanoparticles (NPs) was generated by the toposelective decoration of the TiO2 NTs. Photocatalytic hydrogen evolution was locally triggered with a UV laser beam, and the arrays were screened in the x and y directions for spatially resolved kinetic measurements and the mapping of the optimal hydrogen production. By using this technique, we demonstrate the time-efficient and straightforward determination of the tube dimensions and Pt loading for optimized H2 production. The concept holds promise for generally improving the study of many photoreactions as a function of the physicochemical characteristic of nanophotocatalysts, which renders it highly attractive for the optimization of various important chemical processes.

Preparation and Properties of Electroless Ni-P-<i>β</i>-SiC Composition Coating

Through orthogonal test of Ni-P chemical plating process optimization to determine the optimum process recipe, the Ni-P-β-SiC composite coating were prepared by chemical plating method. The deposition rate, microhardness, composition and organization of Ni-P-β-SiC composite coating are observed and analyzed by scanning electron microscopy (SEM), vivtorinox microhardness tester, X ray diffraction (XRD) and energy spectrum analysis (EDS). The influences of β-SiC particle concentration in bath on the solution of composite coating deposition rate and microhardness. The results show that the influence of composite coating deposition rate and microhardness are NiSO4•6H2O, NaH2PO2•H2O, C3H6O3, and PH. The optimum process conditions: NiSO4•6H2O 25 g/L, NaH2PO2•H2O 27 g/L, C3H6O327 ml/L, PH 5.2. It was found that the Ni-P-β-SiC composite coating with 5g/Lβ-SiC particles exhibited a maximum deposition rate and microhardness, the deposition rate of composite coating is 12.32μm/h, microhardness is 567.93HV0.05.

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RETURN TO ISSUEPREVNewsNEXTNew Journal Editors Named View Author Information C&EN DallasCite this: Chem. Eng. News 2014, 92, 47, 32Publication Date (Print):November 24, 2014Publication History Published online31 March 2015Published inissue 24 November 2014https://doi.org/10.1021/cen-09247-acsnews1Copyright © 2014 Chemical & Engineering NewsArticle Views5Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (326 KB) SUBJECTS:Catalysis,Environmental science,Organic chemistry Get e-Alerts

A Hybrid Method for Stochastic Performance Modeling and Optimization of Chemical Engineering Processes

As chemical engineers seek to improve plant safety, reliability, and financial performance, a wide range of uncertaintyladen decisions need to be made. It is widely agreed that probabilistic approaches provide a rational framework to quantify such uncertainties and can result in improved decision making and performance when compared with deterministic approaches. This article proposes a novel method for design and performance analysis of chemical engineering processes under uncertainty. The framework combines process simulation tools, response surface techniques, and numerical integration schemes applied in structural reliability problems to determine the probability of a process achieving a performance function of interest. The approach can be used to model processes in the presence or absence of performance function(s), with or without parameter interactions, at both design and operational phases. With this, process behavior can be quantified in terms of stochastic performance measures such as reliability indices and the associated most probable process design/operating conditions, providing a simple way to analyze a wide range of decisions. To validate the applicability of the proposed framework, three case study systems are considered: a plug flow reactor, a heat exchanger, and finally a pump system. In each case, performance criteria based on the original physical model and the surrogate model are set up. Reliability analysis is then carried out based on these two models and the results are assessed. The results show that the proposed framework can be successfully applied in chemical engineering analysis with additional benefits over the traditional deterministic methods.

A Novel Hierarchical Control Structure with Controlled Variable Adaptation

For control and optimization of chemical processes, the traditional hierarchical control structure (HCS), where an optimizer in the real-time optimization (RTO) layer updates the set-points of controlled variables (CVs) in the lower control layer, has been well-acknowledged and widely adopted in industrial applications. However, a common drawback of such an HCS is that the speed for a plant to converge to an optimal operation is slow because the optimizer has to wait for the process to settle from one steady-state to another to get an accurate disturbance estimation before making any changes to the set-points. In this Article, a novel HCS based on the concepts of controlled variable adaptation (CVA) and nonoptimality detection is proposed. In the CVA strategy, the CVs are determined and adapted on the basis of a so-called just-in-time regression algorithm to approximate the necessary conditions of optimality (NCO), which makes the self-optimizing performance adaptive to operating condition changes. For no...

Nonlinear programming strategies for dynamic chemical process optimization

Problem formulations and algorithms are considered for optimization problems with differential-algebraic equation (DAE) models. In particular, we provide an overview of direct methods, based on nonlinear programming (NLP), and indirect, or variational, methods. We further classify each method and tailor it to the appropriate applications. For direct methods, we briefly describe current approaches including the sequential approach (or single shooting), multiple shooting method, and the simultaneous collocation (or direct transcription) approach. In parallel to these strategies we discuss NLP algorithms for these methods and discuss optimality conditions and convergence properties. In particular, we present the simultaneous collocation approach, where both the state and control variable profiles are discretized. This approach allows a transparent handling of inequality constraints and unstable systems. Here, large scale nonlinear programming strategies are essential and a novel barrier method, called IPOPT, is described. This NLP algorithm incorporates a number of features for handling large-scale systems and improving global convergence. The overall approach is Newton-based with analytic second derivatives and this leads to fast convergence rates. Moreover, it allows us to consider the extension of these optimization formulations to deal with nonlinear model predictive control and real-time optimization. To illustrate these topics we consider a case study of a low density polyethylene (LDPE) reactor. This large-scale optimization problem allows us to apply off-line parameter estimation and on-line strategies that include state estimation, nonlinear model predictive control and dynamic real-time optimization.

Measurement and Prediction of Thermo Physical Properties to study Intermolecular Interactions of Binary Liquid Mixtures at Various Temperatures by Using McAllister Model

The important of fundamental data for the design and optimization of chemical process are densities and viscosities. The study conducted in this research are viscosities, n, and densities p, of 1,4 Dioxane with two hydrocarbons Benzene and Chloro Benzene have been measured over the entire range of composition at (303, 15, 308,15) K.

Recent Advances in Chemical Process Optimization

AbstractSystematic optimization strategies are essential in process design, operations and control. This study provides a concise overview of recent advances in process optimization, with a particular focus on nonlinear programming. Here, derivative‐free, SQP, reduced gradient, and interior point methods are described. Moreover, state of the art problem formulations and modeling environments are described, and contrasted with popular process simulation tools. Finally research directions are identified for future trends in multi‐scale optimization within large‐scale equation‐oriented optimization environments.

Measurement and Predict Thermo Physical Properties of Binary Liquid Mixtures at Various Temperatures Using Redlich-Kister Model

Excess molar volume and viscosities are one of the important fundamental data for the design and optimization of chemical process. In this study, excess molar volume and viscosities of 1,4 Dioxane with two hydrocarbons Bromobenzene and Ethyl benzene have been measured over the entire range of composition at (303.15, 308.15, and 313.15) K. From experimentations excess volumes, VE, and deviations in viscosities, Δη, are calculated. The calculated excess volumes, VE and deviations in viscosities, Δη exhibited positive and negative values respectively over the whole range of composition in both binary systems. The Redlich-Kister Model was used to correlate excess volumes, VE, and deviations in viscosities, Δη, to derive the binary coefficients and standard deviations of these systems. The fitted outcomes and the calculated data clearly indicated that weak interactions present in two mixtures. It is mainly because of number and position of methyl groups exist in these aromatic hydrocarbons. It can conclude that the experimental values are predicted well by the Redlich-Kister Model with high degree of precision.

Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization

Dynamic optimization problems in chemical processes are often quite challenging because these problems often involve multiple and conflicting objectives. To solve the multi-objective dynamic optimization problems (MDOPs), in this paper, we propose a new multi-objective differential evolution (MODE) variant, named MODE-RMO for short, inspired by the phenomenon that good individuals which contain good information often have more chance to be utilized to guide other individuals. In MODE-RMO, the ranking-based mutation operator is integrated into the MODE algorithm to accelerate the convergence speed, and thus enhance the performance. The performance of our proposed algorithm is firstly evaluated in ten test functions and compared with other MOEAs. The results demonstrate that MODE-RMO can generate Pareto optimal fronts with satisfactory convergence and diversity. Finally, MODE-RMO is applied to solve three MDOPs taken from literature using control vector parameterization. The obtained results indicate that MODE-RMO is an effective and efficient approach for MDOPs.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Plasma enhanced chemical vapor deposition process optimization for thin film silicon tandem junction solar cells

Industry has previously established a 10% stable total module efficiency 1.1×1.3m2 thin film silicon tandem junction (TJ) module manufacturing baseline. A similar silicon TJ cell manufacturing process has been established, using a plasma enhanced chemical vapor deposition (PECVD) tool with an excitation frequency of 13.56MHz, which was modified to handle 30×30cm2 substrates. Extensive PECVD process window characterization was undertaken for the silicon layers that have the biggest impact on device performance. Single layer silicon on glass data and 1×1cm2 single junction and TJ solar cell data for amorphous intrinsic silicon and microcrystalline intrinsic silicon layers have been collected. Single layer characterization data for p-doped microcrystalline silicon oxide layer (μc-SiOx:H-p) for aluminum doped zinc oxide substrates is being presented, as well as solar cell data. The manufacturability of the μc-SiOx:H-p process is being considered. Finally, we present the best solar cell results obtained with the optimized PECVD process, as well as baseline performance data.

探索介尺度科学: 从新角度审视老问题

This paper summarizes the background and research focus of Mechanism and manipulation of mesoscales in multi-phase reaction processes, a major research program under the National Natural Science Foundation of China and its impact on chemical engineering and mesoscience. It argues that the material or interface structure between molecule/atom and bulk material and the heterogeneous structure between particle (droplet or bubble) and unit operation apparatus are bottlenecks in realizing quantitative design, scale-up, optimization and control of chemical processes. These two mesoscale structures and their cross-level linkage are the core challenges of modern chemical engineering science, solution of which will significantly advance chemical engineering and related disciplines to transmit from experience to the state of quantification, thus contributing to the emerging development of interdisciplinary science—Mesoscience.

A software environment for economic NMPC and dynamic real-time optimization of chemical processes

Abstract This paper presents the OptoEcon-Toolbox, a modular software environment for the simulation of chemical and energy processes, and their control using optimizing sampled-data controllers. Key features of this toolbox are modularity, which allows the realization of general multi-layer control architectures, and interfaces to powerful numerical software, which allows the usage of controller and plant surrogate models formulated in different modeling languages such as Modelica or gPROMS. The capability of this toolbox is illustrated in a case study of an industrial copolymerization process.