Optimization Of Chemical Processes Research Articles

A comparison of pulsed and continuous lasers for high‐temperature Raman measurements of anhydrite

AbstractOxygen carrier particles (OCPs) serve as fuel oxidant in emerging chemical looping combustion systems. However, chemical looping combustion process optimization is hindered by the lack of online sensors for measurements of OCP oxidation states at temperatures up to 1,000 °C and pressures up to 10 atm. We are investigating Raman spectroscopy as a potential solution, as this technique is known for its ability to provide noninvasive, molecularly specific information in real time in a wide variety of applications. As a case study, Raman spectra from high‐temperature (>1,000 °C) anhydrite, a potential OCP, are presented using pulsed and continuous wave excitation at 532 nm. When pulsed excitation is coupled with time‐gated detection, background signal from thermal radiation can be significantly reduced from single pulse spectra, outperforming results using an ungated detector. Also, laser heating of the sample is not observed but laser‐induced breakdown spectra increasingly appear with increasing temperature and pulse intensity. Compared with pulsed excitation, continuous wave excitation with longer acquisition time, offers higher signal to noise and avoids the risk for laser‐induced breakdown spectra but demonstrates minor sample heating. Finally, the data demonstrate practical utility by (a) providing a calibration of anhydrite's ν1 (1,017 cm−1) Raman temperature‐dependent band position and by (b) providing estimates of temperature using the ratios of the Stokes/anti‐Stokes ν1 integrated intensities. These results demonstrate the first high‐temperature, pulsed Raman spectra from anhydrite while evaluating challenges associated with high‐temperature, Raman measurements of OCP materials in general.

Optimization under uncertainty in chemical engineering: Comparative evaluation of unscented transformation methods and cubature rules

Model-based optimization under consideration of uncertainty is an important and active research topic. In order to handle uncertain model and process parameters for the design and optimization of chemical processes, various different approaches have been proposed in the literature. Among these approaches, the application of unscented transformation (UT) methods received increasing attention recently. The UT is used to calculate approximations of the first two statistical moments of the objective and constraints which are implemented in the optimization problem. Since the UT only allows for approximations of the statistical moments, the approximation error inevitably influences the optimization. In the present contribution, we emphasize that a poor approximation significantly reduces the meaningfulness of an optimization under uncertainty approach. Hence, we compare a variety of UT methods and study the effect of their corresponding tuning parameters on the approximation accuracy. Furthermore, we investigate four different cubature rules which are well known from the numerical integration literature and which show similarities to the UT from an implementational point of view.The individual methods are compared in terms of approximation accuracy and computational effort based on examples from chemical reaction engineering. Our results indicate that the cubature rule labeled as CU51 provides the most accurate results at reasonable computational costs. The recently proposed cubature rule labeled as MSSRC provides less accurate results, but is computationally more efficient than the CU51 rule and is thus an attractive alternative. Regarding the UT methods, the results show that the proper choice of the tuning parameter significantly influences the optimization results. Furthermore, it is shown that the common tuning parameter selection rule for the original UT does not necessarily yield the most accurate approximation. Hence, based on our results we suggest to favor the application of cubature rules for optimization under uncertainty over the unscented transformation methods.

Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction

The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure–property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C3–C64), covering n-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named B and T0). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trial-and-error process. An artificial neural network was tra...

A Bilevel Programming Approach to Optimize C-phycocyanin Bio-production under Uncertainty

High variability and unreliable expectations on product yields substantially hinder the industrialization of microorganism derived biochemicals as they present a risk to the profitability and safety of the underlying systems. Therefore, in this work, we propose an optimization approach to determine the lower and upper product yield expectations for the sustainable production of C-phycocyanin. Kinetic modeling is adopted in this study as it is an outstanding tool for fast prototyping, prediction and optimization of chemical and biochemical processes. On the upside, parameters in bioprocess kinetic models are used as a simplification of complex metabolic networks to enable the simulation, design and control of the process. On the downside, this conglomeration of parameters may result in significant model uncertainty. To address this shortcoming, we formulate a bilevel max-min optimization problem to obtain the worst-case scenario of our system given the uncertainty on the model parameters. By constructing parameter confidence ellipsoids, we determined the feasible region along which the parameters can minimize the system’s performance, while nutrient and light controls are used to maximize the biorenewable production. The inner minimization problem is embedded by means of the optimality conditions into the upper maximization problem and hence both are solved simultaneously. Through this approach, we determined pessimistic and optimistic scenarios for the bioproduction of C-phycocyanin and hence compute reliable expectations on the yield and profit of the process.

Study on optimization of chemical process based on intelligent computing

In order to solve the problem that the traditional optimization algorithm cannot calculate the optimal solution, this paper proposes an improved intelligent algorithm to get the optimal solution. Artificial fish swarm algorithm (AFSA) is a new research direction of intelligent optimization algorithm, which provides a new theory and new idea for the optimization of complex chemical process. This subject is based on the basic artificial fish swarm algorithm (AFSA). First, the parameters and disadvantages of the algorithm are analyzed, and an improved artificial fish swarm algorithm (IAFSA) that automatically acquires visual perception ranges and steps is proposed. Then, on the basis of several classic test functions, IAFSA's practicality and stability are proven. Finally, IAFSA is applied to the process optimization of the heat transfer pipe network and the optimization of the T alkylation process to verify the practicability of the algorithm in the actual chemical process.

A process simulator interface for multiobjective optimization of chemical processes

The (bio)chemical process industry is under an increasing pressure due to smaller margins and increasing societal and legislative demands for a sustainable future. In this context model-based optimization contributes to the solution because it serves to improve the processes’ performance. Furthermore, multiobjective optimization techniques provide the decision maker with a deeper insight in the tradeoffs when choosing an operating condition. However, an accurate process model is needed to apply these techniques efficiently. In this paper, a novel interface is developed between state-of-the-art gradient-based optimization techniques and the widely used process simulator Aspen Plus. Furthermore, specific challenges and solutions for overcoming the gap between process simulators and optimization tools are highlighted. The resulting interface allows gradient-based techniques to be exploited for optimization of complex industrial processes modeled in the advanced Aspen Plus environment. The interface ensures constraints satisfaction, and a higher computational performance than gradient free methods.

Modeling and Optimization of Chemical Engineering Processes and Systems

Articles prepared based on the materials of the American-Russian Chemical Engineering Scientific School “Modeling and Optimization of Chemical Engineering Processes and Systems”, which was held at Kazan National Research Technological University on May 23−25, 2016, have been reviewed. The development and application of modern simulation and optimization methods for solving the problems of designing, managing, and studying chemical engineering processes and systems for using rationally energy and raw materials, ensure safety and protection of the environment, and provide sustainable development are discussed.

Disjunctive model for the simultaneous optimization and heat integration with unclassified streams and area estimation

In this paper, we propose a disjunctive formulation for the simultaneous chemical process optimization and heat integration with unclassified process streams –streams that cannot be classified a priori as hot or cold streams and whose final classification depend on the process operating conditions–, variable inlet and outlet temperatures, variable flow rates, isothermal process streams, and the possibility of using different utilities.The paper also presents an extension to allow area estimation assuming vertical heat transfer. The model takes advantage of the disjunctive formulation of the ‘max’ operator to explicitly determine all the ‘kink’ points on the hot and cold balanced composite curves and uses an implicit ordering for determining adjacent points in the balanced composite curves for area estimation.The numerical performance of the proposed approach is illustrated with four case studies. Results show that the novel disjunctive model of the pinch location method has excellent numerical performance, even in large-scale models.

Multi‐objective Optimization‐Tool for the Universal Application in Chemical Process Design

AbstractA universally applicable procedure for multi‐objective optimization of chemical processes is developed. A set of known methods and procedures is adapted, combined with newly developed concepts, and integrated into the developed optimization tool, namely, the Adv:ProcessOptimizer. It allows for efficient, comfortable, and robust optimization of a process which is modeled in one of the various linked commercial simulation tools. As a result, the application of the process design with an overlaid optimization is easily accessible for academia and process industry. The industrial styrene process was optimized in order to validate the method. The results show a very densely and mostly equally crowded Pareto front and considerable savings in investment as well as operating costs compared to two reference designs.

Physical–chemical treatment process optimization for high polluting dairy effluents prior fermentation

Among dairy effluents, bactofugate (B) and decreaming racking water (D) were identified as the most polluting due to their organic load content expressed in the chemical oxygen demand (156–240 g·L−1). Joining the plant wastewater, such effluents contribute to the increase of the polluting load of the wastewater treatment plant input which disturbs the treatment performance. This work proposes an upstream segregation of those dairy effluents for combined physical–chemical and biological treatment. An experimental design was proposed to investigate initial pH, applied temperature and exposure time factor effects on the thermal coagulation process. The fermentation of the resulted supernatants using Lactobacillus lactis ssp. lactis was performed. The optimized thermal coagulation pretreatment was obtained at (pH; T(°C); t(min)): 6, 60 °C and 5 min, with both (B) and (D) effluents. Resulted clarified whey sugar, protein and fat contents were assessed. The physical–chemical treatment resulted in considerable organic matter removal: 45% for (B) samples and 31% for (D) samples of proteins content and almost the total fat content. However, there is no considerable effect on the sugar content reduction, which remains responsible for the major fraction of the whey residual chemical oxygen demand (COD). Clarified whey fermentation using Lactococcus lactis ssp. lactis strain induced important sugar consumption rates. Therefore, important sugar consumption rates were recorded and the COD removal efficiency was improved. The recorded global COD removal efficiency was of about 93%. The proposed combined physical–chemical and biological processes for dairy effluents pretreatment allowed not only to reduce the effluents polluting load, but also to valorize wheys by producing valuable components.

Evaluation of two termination criteria in evolutionary algorithms for multi-objective optimization of complex chemical processes

Multi-objective (or multi-criteria) optimization (MOO) is useful for gaining deeper insights into trade-offs among objectives of interest and then selecting one of the many optimal solutions found. It has attracted numerous applications in chemical engineering. Common techniques for MOO are adaptations of stochastic global optimization methods, which include metaheuristics and evolutionary methods, for single-objective optimization. These techniques have been used mostly with maximum number of generations (MNG) as the termination criterion for stopping the iterative search. This criterion is arbitrary and computationally inefficient. Hence, this study investigates two termination criteria based on search progress (i.e., performance or improvement in solutions), for MOO of three complex chemical processes modeled by process simulators, namely, Aspen Plus and Aspen HYSYS. They are Chi-Squared test based Termination Criterion (CSTC) and Steady-State Detection Termination Criterion (SSDTC). Both these criteria are evaluated in two evolutionary algorithms for MOO. Results show that CSTC and SSDTC are successful in giving optimal solutions close to those after MNG but well before MNG. Of the two criteria, CSTC is more reliable and terminates the search earlier, thus reducing computational time substantially.

Sensitivity-based adaptive mesh refinement collocation method for dynamic optimization of chemical and biochemical processes.

Collocation on finite element (CFE) is an effective simultaneous method of dynamic optimization to increase the profitability or productivity of industrial process. The approach needs to select an optimal mesh of time interval to balance the computational cost with desired solution. A new CFE approach with non-uniform refinement procedure based on the sensitivity analysis for dynamic optimization problems is, therefore, proposed, where a subinterval is further refined if the obtained control parameters have significant effect on the performance index. To improve the efficiency, the sensitivities of state parameters with respect to control parameters are derived from the solution of the discretized dynamic system. The proposed method is illustrated by testing two classic dynamic optimization problems from chemical and biochemical engineering. The detailed comparisons among the proposed method, the CFE with uniform mesh, and other reported methods are also carried out. The research results reveal the effectiveness of the proposed approach.

Optimization of chemical bath deposited cadmium sulfide buffer layer for high-efficient CIGS thin film solar cells

An optimization of CdS chemical deposition process is presented in order to fabricate high quality CdS buffer layer for high-performance CIGS thin film solar cells. It is found that the heterogeneous and homogeneous reaction can be regulated conveniently by changing the concentration of cadmium acetate. No obvious large CdS particles on the surface of CdS film can be observed due to the dominant heterogeneous reaction and suppressed homogeneous precipitation under the suitable concentration of cadmium acetate. The device with CdS buffer layer deposited at 0.052M cadmium acetate shows the best efficiency of 11.42%. The performance of the device can be further improved by post-annealing treatment in air. An improved champion efficiency of 12.57% is achieved at the annealing temperature of 180°C.

Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks

In this paper, we introduce the concept of constructing an ontology-based decision support system called J-Park Simulator for the design and operation of eco-industrial parks. It is inspired by Jurong industrial park in Singapore. A biodiesel plant is implemented into the system as a first step. OntoCAPE is adapted for the purposes of the biodiesel plant to establish a knowledge base, which is employed to carry out a number of applications via J-Park Simulator. Firstly, information query can be performed. Information of the biodiesel plant can be extracted through natural language query. Secondly, J-Park Simulator can be used to carry out process simulation. New process equilibrium can be evaluated after certain operation parameters change. Thirdly, process optimization can be realized through J-Park Simulator. Optimal operation condition under different market scenarios can be obtained for the biodiesel producing process in order to reduce the energy consumption and achieve maximal plant profit.

Optimization of Chemical Process with Joint Chance Constraints

In this article, we develop a new approximate method of solving optimization problems with joint chance constraints for the case when the uncertain parameters are independent random variables. We consider only chance constraints with left-hand-side uncertainty. Our method is based on the transformation of chance constraints into deterministic form. This permits the computationally intensive operation of calculating multiple integrals when solving joint chance-constrainted optimization problems to be avoided.

Quantitative and Atomic-Scale View of CO-Induced Pt Nanoparticle Surface Reconstruction at Saturation Coverage via DFT Calculations Coupled with in Situ TEM and IR.

Atomic-scale insights into how supported metal nanoparticles catalyze chemical reactions are critical for the optimization of chemical conversion processes. It is well-known that different geometric configurations of surface atoms on supported metal nanoparticles have different catalytic reactivity and that the adsorption of reactive species can cause reconstruction of metal surfaces. Thus, characterizing metallic surface structures under reaction conditions at atomic scale is critical for understanding reactivity. Elucidation of such insights on high surface area oxide supported metal nanoparticles has been limited by less than atomic resolution typically achieved by environmental transmission electron microscopy (TEM) when operated under realistic conditions and a lack of correlated experimental measurements providing quantitative information about the distribution of exposed surface atoms under relevant reaction conditions. We overcome these limitations by correlating density functional theory predictions of adsorbate-induced surface reconstruction visually with atom-resolved imaging by in situ TEM and quantitatively with sample-averaged measurements of surface atom configurations by in situ infrared spectroscopy all at identical saturation adsorbate coverage. This is demonstrated for platinum (Pt) nanoparticle surface reconstruction induced by CO adsorption at saturation coverage and elevated (>400 K) temperature, which is relevant for the CO oxidation reaction under cold-start conditions in the catalytic convertor. Through our correlated approach, it is observed that the truncated octahedron shape adopted by bare Pt nanoparticles undergoes a reversible, facet selective reconstruction due to saturation CO coverage, where {100} facets roughen into vicinal stepped high Miller index facets, while {111} facets remain intact.

Numerical simulation of unsteady dense granular flows with rotating geometries

In chemical engineering applications, it is not uncommon to encounter reactors featuring rotating parts. As these rotating parts are present in order to enhance processes such as chemical reactions and/or ensure homogeneity, it is essential to take them into account to perform predictive numerical simulations. This aspect can be particularly challenging, even more so when complex industrial geometries are to be treated.In this paper a numerical methodology for simulating unsteady granular flow in rotating geometries is presented. The method is based on splitting the domain into static and rotating parts. The information between rotating and static parts is passed by a non-conformal mesh matching technique. The presented methodology is validated numerically by comparing its results with other conventional methods. The method is then applied to an industrial scale problem. The applicability of the method and the way it may be used to investigate complex flow is demonstrated.Therefore this approach enables to consider the full geometry of complex reactors. It opens the door to further investigation, optimization and design of industrial scale chemical processes.

Optimization of chemical modification process of activated carbon surface with iron nanoparticles for efficient vanadium removal: Kinetics, equilibrium and surface complexation modelling

Optimization of chemical modification process of activated carbon surface with iron nanoparticles for efficient vanadium removal: Kinetics, equilibrium and surface complexation modelling

Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes

Energy optimization and analysis of complex chemical processes play a significant role in the sustainable development procedure. In order to deal with the high-dimensional and noise data in complex chemical processes, we present an energy optimization and analysis method based on extreme learning machine integrating the index decomposition analysis. First, index decomposition analysis has been used to decompose the high-dimensional data to three energy performance indexes of the activity effect, the structure effect and the intensity. And then, those indexes and the production/conductivity of the chemical process are defined as inputs and outputs of the extreme learning machine respectively to build energy optimization and analysis model. Finally, the proposed method has been applied to optimizing and analyzing energy status of the ethylene system and the purified terephthalic acid solvent system in complex chemical processes. The experiment results show that the proposed method has the characteristics of fast learning, stable network outputs and high model accuracy in handling with the high-dimensional data. Moreover, it can optimize energy of chemical processes and guide the production operation. In our experiment, the production of ethylene plants can be increased by 5.33%, and the conductivity of purified terephthalic acid plants can be reduced by 0.046%.

Predicting PR EOS binary interaction parameter using readily available molecular properties

The proper design, operation, simulation and optimization of numerous chemical processes require the knowledge of phase behavior of coexisting phases over wide ranges of pressure and temperature. A convenient method for describing equilibrium phase behavior is use of analytic equations of state. The most widely used equations of state (EOS) are the cubic van der Waals type equations such as the Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) EOS. These equations utilize one or two empirical binary interaction parameters (BIPs) for each binary pair in a mixture. These BIPs can have a significant effect on the predicted properties of mixtures and are generally required for accurate predictions.In a recent work, we developed generalized models to estimate the BIPs in the PR EOS for non-ideal, low-pressure systems using a quantitative structure–property relationship (QSPR) modeling approach. In a departure from our earlier work, we present in this study a different modeling approach which utilizes only readily available molecular properties of the components involved in a mixture. This greatly simplifies application of our new models, since they do not require elaborate computational methods for generation of molecular descriptors.To develop the present generalized model, a VLE database consisting of 1010 binary systems comprised of non-ideal, low-pressure binary systems as well as asymmetric, high-pressure binary systems was utilized. Using this database, the BIPs for the PR EOS were first determined by regressing the VLE experimental data for each system. Then, the BIPs were generalized in terms of molecular properties of components in a mixture. Results indicate that the generalized model provides bubble-point pressure predictions with an average absolute deviation (AAD) of 9% when compared with an AAD of 4.7% through direct regressions of the BIPs. Further, the model developed in this study was compared to our previous model by predicting bubble-point pressures of non-ideal, low-pressure binary systems. The predictions from the model developed in this study were comparable to our previous results (9.2% compared to 8.3% AAD), thus demonstrating the usefulness of the simpler approach presented in this work.