Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front

Economic and environmental impacts of the energy source for the utility production system in the HDA process

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a Multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of Multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This ‘‘gate-to-gate’’ environmental study is then enlarged by implementing a ‘‘cradle-togate’’ Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development

Investigation of Separation Efficiency Indicator for the Optimization of the Acetone–Methanol Extractive Distillation with Water.

A multiobjective genetic algorithm optimization of the extractive distillation process of acetone–methanol minimum azeotropic mixture with heavy entrainer water is investigated. The process includes the extractive and entrainer regeneration columns, and the optimization minimizes the energy cost objective function (OF) and total annual cost (TAC) and maximizes efficiency indicators Eext and eext that describe the ability of the extractive section to discriminate the product between the top and the bottom of that section. Earlier work (You et al. Ind. Eng. Chem. Res.2015, 54, 491) found that improvement of some designs in the literature led to an increase in those indicators. A two-step optimization strategy for extractive distillation is conducted to find suitable values of the entrainer feed flow rate, entrainer and azeotropic mixture feed locations, total number of trays, two reflux ratios, and two distillates in both the extractive column and the entrainer regeneration column. The first step relies upon the use of a nonsorted genetic algorithm (NSGA) with the four aforementioned criteria. Second, the best design taken from the GA Pareto front is further optimized focusing on decreasing the energy cost by using a sequential quadratic programming (SQP) method. In this way, the most suitable design with optimal efficiency indicators, low energy consumption, and low cost are obtained. Analyzed with respect to thermodynamic insights underlying the extractive section composition profile map, the Pareto front results show that there is maximum Eext at a given reflux ratio, and there is minimum reflux ratio for a given Eext. There is an optimal efficiency indicator Eext,opt which corresponds to the minimum TAC taken as the best design. In other words, Eext,opt can be a criterion for the comparison between different designs for the same separating system. A SQP-based design is only <1% better in TAC than the best NSGA design, showing that this later method is able to find a consistent design for the extractive process concerning the 1.0-1a class mixture

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.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)

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.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)

Thermodynamic Insight for the Design and Optimization of Extractive Distillation of 1.0-1a Class Separation

Nous étudions la distillation extractive continue de mélanges azéotropiques à temperature de bulle minimale avec un entraineur lourd (classe 1.0-1a) avec comme exemples les mélanges acétone-méthanol avec l’eau et DIPE-IPA avec le 2-méthoxyethanol. Le procédé inclut les colonnes de distillation extractive et de régénération de l’entraineur en boucle ouverte et en boucle fermée. Une première stratégie d’optimisation consiste à minimiser la fonction objectif OF en cherchant les valeurs optimales du débit d’entraineur FE, les positions des alimentations en entraineur et en mélange NFE, NFAB, NFReg, les taux de reflux R1, R2 et les débits de distillat de chaque colonne D1, D2. OF décrit la demande en énergie par quantité de distillat et tient compte des différences de prix entre les utilités chaudes et froides et entre les deux produits. La deuxième stratégie est une optimisation multiobjectif qui minimise OF, le coût total annualisé (TAC) et maximise deux nouveaux indicateurs thermodynamiques d’efficacité de séparation extractive totale Eext et par plateau eext. Ils décrivent la capacité de la section extractive à séparer le produit entre le haut et le bas de la section extractive. L’analyse thermodynamique des réseaux de courbes de résidu ternaires RCM et des courbes d’isovolatilité montre l’intérêt de réduire la pression opératoire dans la colonne extractive pour les séparations de mélanges 1.0-1a. Une pression réduite diminue la quantité minimale d’entraineur et accroît la volatilité relative du mélange binaire azéotropique dans la région d’opération de la colonne extractive. Cela permet d’utiliser un taux de reflux plus faible et diminue la demande énergétique. La première stratégie d’optimisation est conduite avec des contraintes sur la pureté des produits avec les algorithmes SQP dans les simulateurs Aspen Plus ou Prosim Plus en boucle ouverte. Les variables continues optimisées sont : R1, R2 et FE (étape 1). Une étude de sensibilité permet de trouver les valeurs de D1, D2 (étape 2) et NFE, NFAB, NFReg (étape 3), tandis l’étape 1 est faite pour chaque jeu de variables discrètes. Enfin le procédé est resimulé en boucle fermée et TAC, Eext et eext sont calculés (étape 4). Les bilans matières expliquent l’interdépendance des débits de distillats et des puretés des produits. Cette optimisation permet de concevoir des procédés avec des gains proches de 20% en énergie et en coût. Les nouveaux procédés montrent une amélioration des indicateurs Eext et eext. Afin d’évaluer l’influence de Eext et eext sur la solution optimale, la seconde optimisation multiobjectif est conduite. L’algorithme génétique est peu sensible à l’initialisation, permet d’optimiser les variables discrètes N1, N2 et utilise directement le shéma de procédé en boucle fermée. L’analyse du front de Pareto des solutions met en évidence l’effet de FE/F et R1 sur TAC et Eext. Il existe un Eext maximum (resp. R1 minimum) pour un R1 donné (resp. Eext). Il existe aussi un indicateur optimal Eext,opt pour le procédé optimal avec le plus faible TAC. Eext,opt ne peut pas être utilisé comme seule fonction objectif d’optimisation mais en complément des autres fonctions OF et TAC. L’analyse des réseaux de profils de composition extractive explique la frontière du front de Pareto et pourquoi Eext augmente lorsque FE diminue et R1 augmente, le tout en lien avec le nombre d’étage. Visant à réduire encore TAC et la demande énergétique nous étudions des procédés avec intégration énergétique double effet (TEHI) ou avec des pompes à chaleur (MHP). En TEHI, un nouveau schéma avec une intégration énergétique partielle PHI réduit le plus la demande énergétique. En MHP, la recompression partielle des vapeurs VRC et bottom flash partiel BF améliorent les performances de 60% et 40% respectivement. Au final, le procédé PHI est le moins coûteux tandis que la recompression totale des vapeurs est la moins énergivore. ABSTRACT : We study the continuous extractive distillation of minimum boiling azeotropic mixtures with a heavy entrainer (class 1.0-1a) for the acetone-methanol with water and DIPE-IPA with 2-methoxyethanol systems. The process includes both the extractive and the regeneration columns in open loop flowsheet and closed loop flowsheet where the solvent is recycled to the first column. The first optimization strategy minimizes OF and seeks suitable values of the entrainer flowrate FE, entrainer and azeotrope feed locations NFE, NFAB, NFReg, reflux ratios R1, R2 and both distillates D1, D2. OF describes the energy demand at the reboiler and condenser in both columns per product flow rate. It accounts for the price differences in heating and cooling energy and in product sales. The second strategy relies upon the use of a multi-objective genetic algorithm that minimizes OF, total annualized cost (TAC) and maximizes two novel extractive thermodynamic efficiency indicators: total Eext and per tray eext. They describe the ability of the extractive section to discriminate the product between the top and to bottom of the extractive section. Thermodynamic insight from the analysis of the ternary RCM and isovolatility curves shows the benefit of lowering the operating pressure of the extractive column for 1.0-1a class separations. A lower pressure reduces the minimal amount of entrainer and increases the relative volatility of original azeotropic mixture for the composition in the distillation region where the extractive column operates, leading to the decrease of the minimal reflux ratio and energy consumption. The first optimization strategy is conducted in four steps under distillation purity specifications: Aspen Plus or Prosim Plus simulator built-in SQP method is used for the optimization of the continuous variables: R1, R2 and FE by minimizing OF in open loop flowsheet (step 1). Then, a sensitivity analysis is performed to find optimal values of D1, D2 (step 2) and NFE, NFAB, NFReg (step 3), while step 1 is done for each set of discrete variables. Finally the design is simulated in closed loop flowsheet, and we calculate TAC and Eext and eext (step 4). We also derive from mass balance the non-linear relationships between the two distillates and how they relate product purities and recoveries. The results show that double digit savings can be achieved over designs published in the literature thanks to the improving of Eext and eext. Then, we study the influence of the Eext and eext on the optimal solution, and we run the second multiobjective optimization strategy. The genetic algorithm is usually not sensitive to initialization. It allows finding optimal total tray numbers N1, N2 values and is directly used with the closed loop flow sheet. Within Pareto front, the effects of main variables FE/F and R1 on TAC and Eext are shown. There is a maximum Eext (resp. minimum R1) for a given R1 (resp. Eext). There exists an optimal efficiency indicator Eext,opt which corresponds to the optimal design with the lowest TAC. Eext,opt can be used as a complementary criterion for the evaluation of different designs. Through the analysis of extractive profile map, we explain why Eext increases following the decrease of FE and the increase of R1 and we relate them to the tray numbers. With the sake of further savings of TAC and increase of the environmental performance, double-effect heat integration (TEHI) and mechanical heat pump (MHP) techniques are studied. In TEHI, we propose a novel optimal partial HI process aiming at the most energy saving. In MHP, we propose the partial VRC and partial BF heat pump processes for which the coefficients of performance increase by 60% and 40%. Overall, optimal partial HI process is preferred from the economical view while full VRC is the choice from the environmental perspective

Hybrid simulation-equation based synthesis of chemical processes

A challenging problem in the synthesis and design of chemical processes consists of dealing with hybrid models involving process simulators and explicit constraints. Some unit operations in modular process simulators are slightly noisy or require large CPU times to converge. In this work, this problem is addressed by combining process simulators and surrogate models. We have replaced some unit operations, which cannot be used directly with a gradient-based optimization, by surrogate models based on Kriging interpolation. To increase the robustness of the resulting optimization model, we perform a degree of freedom analysis and aggregate (or disaggregate) parts of the model to reduce the number of independent variables of the Kriging surrogate models (KSMs). Thus, the final model is composed of KSMs, unit operations (maintained in the process simulator) and also explicit equations. The optimization of the well-known vinyl chloride monomer (VCM) production process is performed to test the proposed approach. The effect of the heat integration is also studied. In addition, the economic feasibility of the optimized process is calculated assuming uncertainty in raw material and product prices.The authors gratefully acknowledge the financial support by the Ministry of Economy and Competitiveness from Spain, under the project CTQ2016-77968-C3-02-P (AEI/FEDER, UE), and Call 2013 National Sub-Program for Training, Grants for pre-doctoral contracts for doctoral training (BES-2013-064791)

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.The authors wish to acknowledge the financial support by the Ministry of Economy and Competitiveness of Spain, under the project CTQ2012-37039-C02-02

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Методологија за синтезу реактора заснована на концептима интензификације процеса и примени метода оптимизације

In this Ph.D. thesis, a new methodology for Reactor Synthesis Based on Process Intensification Concepts and Application of Optimization Methods (ReSyPIO) is presented and applied to two different cases. In Chapter 1: Introduction – Motivation and Objectives, the motive for the research is presented, and Hypotheses are formulated. The ReSyPIO methodology that rests upon these Hypotheses and consists of three consecutive stages is briefly described in this Chapter. The first stage encapsulates all present phases and phenomena inside the reactor functional building block, called module. Modules come as a direct result of a conceptual representation of the analyzed system. In the second stage, modules are further segmented if needed and interconnected, creating a reactor superstructure that is mathematically described for all desirable operating regimes. In the last stage of the ReSyPIO methodology, the optimal structure, operating conditions, and the operational regime are determined with the use of rigorous optimization. All three stages of the ReSyPIO methodology have a backflow, meaning that if analysis leads to impractical, nonfunctional or inefficient results, modifications in reactor superstructure and modules can be made. The objective is to conceptually and numerically derive the most efficient reactor structure and a set of operating conditions that would be used as a starting point in the future reactor design. Chapter 2: Literature Review is used to cover and review the most important research published in the area of Process Intensification and different Process System Engineering techniques. Different approaches and studies present in academia are highlighted and their elements compared with the presented ReSyPIO methodology with the accent on its advantages and contribution to the engineering science community.Also, in this Chapter, an array of well researched analytical and numerical approaches is presented that could be used in the future to strengthen the ReSyPIO methodology further and facilitate its easier application. In Chapter 3: Description of the ReSyPIO Methodology Reactor Synthesis based on Process Intensification and Optimization of Superstructure is explained in detail, with a graphical representation of the main building block, called Phenomenological Module. A general explanation is given on how to form a reactor superstructure and mathematically describe it with sets of material and energy balance equations that correspond to a number of present phases and components in the system. The ReSyPIO methodology is first applied to a generic case of two parallel reactions in Chapter 4, called Application of the ReSyPIO Methodology on a Generic Reaction Case. The case corresponds to two parallel reactions that could be found in the fine chemical industry. The reactions are endothermic and slow with the undesired product. After the application of the ReSyPIO methodology, an optimal reactor structure consisting of a segmented module with 17 side inlets for the reactant and heat source is obtained. It is recommended for the reactor to work in a continuous steady-state mode as the dynamic operation would not lead to a sufficient increase in reactor efficiency...У овој докторској дисертацији је представљена и примењена нова методологија за синтезу реактора заснована на концептима интензификације процеса и примени различитих оптимизационих техника (Reactor Synthesis Based on Process Intensification Concepts and Application of Optimization Methods – ReSyPIO). У поглављу Увод – Мотивација и циљеви, формиране су хипотезе на којима почива ReSyPIO методологија и дата је мотивација за истраживање. ReSyPIO методологија је укратко представљена и описана кроз три узастопне етапе. Прва етапа уоквирава све присутне фазе и феномене у реактору унутар функционалних градивних јединица, названих модули. Модули представљају резултат концептуалног приказа анализираног система. У другој етапи, модули се по потреби могу даље поделити у сегменте и међусобно повезати, креирајући суперструктуру реактора. Суперструктура је математички описана за све режиме рада реактора од интереса. У последњој етапи ReSyPIO методологије, оптимална структура, услови и режим рада реактора су одређени применом ригорозне оптимизације. Све три етапе ReSyPIO методологије имају повратни ток, што значи да уколико анализа води ка непрактичним, нефункционалним или неефикасним решењима, модификација математичког модела, суперструктуре и/или модула је могућа. Циљ примене ReSyPIO методологије је да се концептуалним и нумеричким приступом дође до оптималне препоруке за структуру реактора, оперативне услове и режим рада, која би била почетна претпоставка у будућем дизајну уређаја. Преглед литературе даје опис и приказ свих истраживања од интереса, из области Интензификације процеса и Теорије и анализе процесних система. Наглашени су различити приступи и студије присутне у истраживачкојзаједници, а њихови елементи упоређени са представљеном ReSyPIO методологијом са акцентом на предностима и научном доприносу. У овом поглављу је дат и низ добро истражених аналитичких и нумеричких приступа који би могли да буду коришћени у оквиру ReSyPIO методологије и олакшају њену примену. У поглављу Опис ReSyPIO методологије, је детаљно објашњена синтеза реактора заснована на концептима интензификације процеса и оптимизацији суперструктуре. Прво је дата процедура за графичку и концептуалну репрезентацију система, преко главних градивних јединица, феноменолошких модула. Потом је објашњено како се креира суперструктура реактора. На крају је дат уопштен поступак за математички опис суперструктуре преко скупова једначина материјалног и енергетског биланса, чији број зависи од броја присутних фаза и компонената у систему. ReSyPIO методологија је први пут примењена на случају две генеричке паралелне реакције у поглављу под називом Примена ReSyPIO методологије на случају генеричке реакције. Овај случај одговара реакцијама које се могу наћи у индустрији финих хемикалија. Реакције су ендотермне и споре, при чему је кинетички фаворизовано креирање нежељеног производа. Након примене ReSyPIO методологије, добијена је оптимална структура реактора која се састоји од сегментисаног модула са 17 улаза за извор топлоте и реактант који се дозира. Предложено је да реактор ради континуално, у стационарном режиму рада, јер би динамички режим рада резултовао недовољним повећањем ефикасности реактора..