A modeling framework for design of nonlinear renewable energy systems through integrated simulation modeling and metaheuristic optimization: Applications to biorefineries

This study presents the development and implementation of a novel framework for optimal design of new and emerging renewable energy production systems by considering an iterative strategy which integrates the Net Present Value optimization along with detailed mechanistic modeling, simulation, and process optimization which yields optimal capacity plan, and operating conditions for the process. Due to the non-linear nature of process conversion mechanisms, metaheuristic algorithms are implemented in the framework to optimize operating conditions of process. Further, to apply complex kinetics in the process, we have made a linkage between process simulator (Aspen Plus) and Matlab. To demonstrate the effectiveness of the proposed methodology, a hypothetical case study of a lignocellulosic biorefinery is utilized. The proposed framework results reveal a deviation in optimal process yields and production capacities from initial literature estimates. These results indicate the importance of developing a multi-layered framework to optimally design a renewable energy production system.     

A multiobjective optimization framework for design of integrated biorefineries under uncertainty

A systematic approach for development of a reliable optimization framework to address the optimal design of integrated biorefineries in the face of uncertainty is presented. In the current formulation, a distributed strategy which is composed of different layers including strategic optimization, risk management, detailed mechanistic modeling, and operational level optimization is applied. In the strategic model, a multiobjective stochastic optimization approach is utilized to incorporate the tradeoffs between the cost and the financial risk. Then, Aspen Plus models are built to provide detailed simulation of biorefineries. In the final layer, an evolutionary algorithm is employed to optimize the operating condition. To demonstrate the effectiveness of the framework, a hypothetical case study referring to a multiproduct lignocellulosic biorefinery is utilized. The numerical results reveal the efficacy of the proposed approach; it provides decision makers with a quantitative analysis to determine the optimum capacity plan and operating conditions of the biorefinery. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3208–3222, 2015     

Synergistic optimization framework for the process synthesis and design of biorefineries

The conceptual process design of novel bioprocesses in biorefinery setups is an important task,which remains yet challenging due to several limitations.We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically.In this context,several approaches for superstructure optimization based on different surrogate models can be deployed.By means of a case study,the framework is introduced and validated,and the different superstructure optimization approaches are benchmarked.The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues,there remains a potential issue regarding their validation.The development of appropriate surrogate models,comprising the selection of surrogate type,sampling type,and size for training and cross-validation sets,are essential factors.Regarding this aspect,satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem.Furthermore,the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulationbased optimization to consolidate the process design under uncertainty offer an alternative and promising approach.These findings invite for a critical assessment of surrogatebased optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.     

Pinch‐based shortcut method for the conceptual design of adiabatic absorption columns

Shortcut methods are valuable tools for the fast evaluation of key performance indicators in the early phase of conceptual process design. For the design of absorption columns, operation at minimum solvent demand represents a thermodynamically sound indicator, which is, however, difficult to determine because an infinite number of separation stages need to be considered. Instead, the suggested shortcut model exploits the existence of the pinch point to identify operation at minimum solvent demand. Existing shortcut concepts, such as the well‐known equation of Kremser (Natl Pet News, 22, 43–49, 1930), are significantly outperformed by the novel shortcut model, which can be gradually refined to any desired accuracy. Integration into a stepwise procedure results in reliable solutions. The model covers rigorous thermodynamics; no simplifications regarding phase equilibrium, heat effects, or number of components are required. The performance of the method is illustrated by several case studies with up to seven components. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1213–1225, 2017     

Pinch‐based shortcut method for the conceptual design of isothermal extraction columns

Shortcut methods are valuable tools for a comprehensive evaluation of key performance indicators in the early phase of conceptual process design. For the design of extraction columns, operation at minimum solvent demand represents a thermodynamically sound indicator, which is, however, difficult to determine. The suggested shortcut model therefore exploits the existence of the pinch point to directly identify operation at minimum solvent demand. It is solved quickly and reliably by a step‐by‐step procedure. The final step allows a reduction of the approximation error to any desired degree of accuracy. No simplifications regarding the number of components in the mixture or its thermodynamic behavior are introduced. Hence, arbitrary mixtures can be tackled. The performance of the method is highlighted by a fully automated screening of thousands of solvents for the recovery of fermentation products acetone, 1‐butanol, and ethanol from aqueous solution. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1236–1245, 2017     

A hybrid optimisation model for the synthesis of sustainable gasification-based integrated biorefinery

Depletion of fossil fuels and increasing public awareness of environmental issues has stimulated the search for alternative energy sources. Biofuels are recognised as one of the most promising alternatives to fossil fuels, as they can be produced from various types of feedstock. The efficiency and sustainability of biomass-based production can be maximised by producing biofuels along with other valuable coproducts in a “biorefinery”. This concept was proposed to make the production of biofuels and biochemicals more economically viable by taking advantage of opportunities for process integration and waste recovery. In this work, a novel hybrid optimisation model that combines superstructure-based optimisation approach and insight-based automated targeting for the synthesis of a sustainable integrated biorefinery is presented. In addition, fuzzy optimisation is also adapted to synthesize such integrated facility with the simultaneous consideration of both economic and environmental performance. Note that the proposed approach is a generic synthesis strategy that can be applied even without detailed modelling of individual processes.     

An integrated mathematical programming approach for the design and optimisation of offshore fields

The paper presents a systematic methodology for the optimal design and operational management of offshore oil fields. It is comprised of two stages. At the design stage, the optimal production capacity of a main field is determined with an adjacent satellite field and a well drilling schedule. The problem is formulated as a mixed-integer linear programming formulation. Continuous variables represent individual well, jacket and topsides costs. Binary variables are used to select individual wells within a defined field grid. The mathematical formulation is concise and efficient. An MINLP model is proposed for the operational management optimisation of the offshore oilfields. In the latter model, non-linear equations are extensively used to model the pressure drops in pipes and wells for multiphase flow. Non-linear cost equations have been derived for the production costs of each well accounting for the length, the production rate and their maintenance. Operational decisions determine the oil flowrates, the operation/shut-in for each well and the pressures for each point in the piping network.     

Optimization of process synthesis and design problems: A modified differential evolution approach

A large number of process synthesis and design problems in chemical engineering can be modeled as mixed integer nonlinear programming (MINLP) problems. They involve continuous (floating point) and integer variables. A common feature of this class of mathematical problems is the potential existence of non-convexities due to the particular form of the objective function and/or the set of constraints. Due to their combinatorial nature, these problems are considered to be difficult. In recent years, evolutionary algorithms (EAs) are gaining popularity for finding the optimal solution of nonlinear multimodal problems encountered in many engineering disciplines. In the present study, a novel modified differential evolution [Angira, R., Babu, B.V., 2005a. Optimization of non-linear chemical processes using modified differential evolution (MDE). Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI-05), Pune, India, December 20-22, pp. 911-923. Also available at 〈http://discovery.bits-pilani.ac.in/discipline/chemical/bvb/publications.html〉], one of the evolutionary algorithms, is used for solving process synthesis and design problems. To illustrate the applicability and efficiency of modified differential evolution (MDE), seven test problems on process synthesis and design have been solved. These problems arise from the area of chemical engineering, and represent difficult nonconvex optimization problems, with continuous and discrete variables. The performance of MDE is compared with that of Genetic Algorithm, Evolution Strategy, and MINLP-Simplex Simulated Annealing (M-SIMPSA).     

A complete testing environment for the automated parallel performance characterization of biofuel cells: design,validation, and application

We present a complete testing environment for the parallel performance characterization of biofuel cells. Besides rapid-assembly electrode fixtures and an aseptic electrochemical reactor, it comprises a 24-channel electrical testing system that bridges the gap between simple load resistors and costly multi-channel potentiostats. The computer-controlled testing system features active current control to enable the forced operation of half-cell electrodes, whereas galvanic isolation between individual channels ensures interference-free operation of multiple fuel cells immersed in a common testing solution. Implemented into the control software is an automated procedure for the step-wise recording of polarization curves. This way, performance overestimation due to a too fast increase in load current can be circumvented. As an applicational example, three abiotically catalyzed glucose fuel cells are characterized simultaneously in a common testing solution. Complete disclosure of the electrical system (incl. printed circuit board layout, control software, and circuit diagrams) in the online supplementary material accompanying this paper allows researchers to replicate our setup in their lab and can serve as inspiration for the design of similar systems adapted to specific requirements. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A new integrated approach of coal gasification: the concept and preliminary experimental results

The total carbon conversion of conventional fluidized bed gasifier is relatively low (<90%) mainly because of carbon loss in fly-ash. In this paper, a new concept of integrated coal gasification—fluidized bed+entrained flow is introduced. Within this process, large partition of coal with higher reactivity is converted in an ash agglomerating fluidized bed reactor under moderate temperature (~1000 °C). The remaining small partition of coal (fly-ash) with lower reactivity is converted in a small integrated entrained flow gasifier under higher temperature (1200–1400 °C). Low carbon content ash is withdrawn in dry mode by ash agglomerating, with no need to be melted. Preliminary experimental results show that the whole system can be operated steadily, total carbon conversion reaches >95%, efficient gas (CO+H₂) concentration is 78–82%. Heat exchange between two reactors has been realized, the high temperature gas from entrained flow gasifier can be cooled, and in the mean time the temperature of fluidized bed nearly keeps constant. The high-temperature ash from entrained flow gasifier can be cooled by the char in dense phase of the fluidized bed and then withdrawn in agglomerating mode. All these results prove the concept correct and feasible.     

A stochastic programming approach for the Bayesian experimental design of nonlinear systems

Several approaches for the Bayesian design of experiments have been proposed in the literature (e.g., D-optimal, E-optimal, A-optimal designs). Most of these approaches assume that the available prior knowledge is represented by a normal probability distribution. In addition, most nonlinear design approaches involve assuming normality of the posterior distribution and approximate its variance using the expected Fisher information matrix. In order to be able to relax these assumptions, we address and generalize the problem by using a stochastic programming formulation. Specifically, the optimal Bayesian experimental design is mathematically posed as a three-stage stochastic program, which is then discretized using a scenario based approach. Given the prior probability distribution, a Smolyak rule (sparse-grids) is used for the selection of scenarios. Two retrospective case studies related to population pharmacokinetics are presented. The benefits and limitations of the proposed approach are demonstrated by comparing the numerical results to those obtained by implementing a more exhaustive experimentation and the D-optimal design.     

Optimal processing pathway for the production of biodiesel from microalgal biomass: A superstructure based approach

In this study, we propose a mixed integer nonlinear programming (MINLP) model for superstructure based optimization of biodiesel production from microalgal biomass. The proposed superstructure includes a number of major processing steps for the production of biodiesel from microalgal biomass, such as the harvesting of microalgal biomass, pretreatments including drying and cell disruption of harvested biomass, lipid extraction, transesterification, and post-transesterfication purification. The proposed model is used to find the optimal processing pathway among the large number of potential pathways that exist for the production of biodiesel from microalgae. The proposed methodology is tested by implementing on a specific case with different choices of objective functions. The MINLP model is implemented and solved in GAMS using a database built in Excel. The results from the optimization are analyzed and their significances are discussed.     

A Bayesian experimental design approach for assessing new product performance: An application to disinfectant formulation

This paper presents a Bayesian methodology for computer‐aided experimental design for hydrogen peroxide formulations. Hydrogen peroxide is one of the oldest known active antimicrobial chemicals and is used in many cleaning/disinfecting formulations. It is favourable as an active antimicrobial in that it degrades only to water and oxygen, and does not contaminate the environment. However hydrogen peroxide is difficult to stabilise, and disinfecting products based on it soon lose their antimicrobial activity. Moreover, regulatory agencies such as U.S. Environmental Protection Agency (EPA) and Health Canada require that disinfecting products do not lose more than 5–10% of their active concentration throughout their shelf life. Therefore, it is very important while formulating hydrogen peroxide‐based products to test for their stability. An effective way to improve hydrogen peroxide stability in a solution is to use stabilisers. It is desired to use these chemicals in as low concentrations as possible for environmental and economic considerations. On the other hand, due to tight market competition, the new products need to be formulated as quickly as possible, and therefore there is limited time to ensure product stability. In this paper, prior information has been used in the form of a model, based on historical experiments. A Bayesian D‐optimality criterion is used to design a few additional experiments so that the resulting model can have an acceptable prediction power. It is shown that a design which uses the Bayesian D‐optimality criterion taking advantage of prior information can be more efficient than even a resolution IV fractional factorial design in the sense that using fewer trials gives a model with equivalent prediction capability. This can be critical where experiments are expensive to perform.     

A combined method for the design and optimization of intensified distillation systems

The sequential design method (SDM) is a recently introduced distillation column design method based on the correspondence between the simple column and the alternative sequences design parameters. It can be defined as a method that combines the alternatives generation step with the design procedure. The Multi-Objective Differential Evolution (MODE) algorithm instead, is based on the minimization or maximization of a defined objective function; it is a strong method that allows the evaluation of a large number of alternatives.The SDM is characterized by a poor control of the error propagation among the sequences, the MODE algorithm by a high computational time. It was obtained that a proper combination of the methods allows the compensation of the drawbacks of the stand-alone procedures. For the case study considered, when the MODE algorithm is initialized with the SDM, a 39% reduction of the computational time was observed. If the combination between the methods is realised at the beginning of the design procedure, the reduction of the computational time was of about 28%. The time reduction was due to a narrower initialization of the MODE algorithm with the parameters obtained applying the SDM. Moreover, when some overestimated design values where used to initialize the MODE algorithm, it is still possible to identify a configuration very close to the optimum.     

A numerical method of cascade analysis and design for multi-component isotope separation

A numerical method is presented for cascade analysis and design for multi-component isotope separations. A fundamental issue of interest in cascade analysis and design is the solution of the nonlinear algebraic equation system. This system describes the mass conservation and the separation property of a cascade, which provides the hydraulic state and the component distributions in the cascade. Analytical solutions for the system are only available in a few special cases. Numerical methods are preferred for many of the complicated separation situations; however, the solution process in a numerical method is carried out through iterations and is very sensitive to initial values, which often leads to the failure of the method. Continuation techniques satisfactorily resolve the problem of sensitivity. This paper presents computer algorithms in detail to show how the techniques were implemented in order to tackle the problems in constructing different cascades. Various cascades were employed as test problems, including the well-known matched-R cascade, the quasi-ideal cascade, the less-known matched-X cascade and a newly invented pseudo-binary cascade. Numerical experiments on these cascades demonstrated that these cascades are readily solved with the required properties and that the proposed method is a powerful technique for analyzing and designing isotope separation cascades.     

Synthesis of sustainable integrated biorefinery via reaction pathway synthesis: Economic,incremental enviromental burden and energy assessment with multiobjective optimization

With the increasing attention toward sustainable development, biomass has been identified as one of the most promising sources of renewable energy. To convert biomass into value‐added products and energy, an integrated processing facility, known as an integrated biorefinery is needed. To date, various biomass conversion systems such as gasification, pyrolysis, anaerobic digestion and fermentation are well established. Due to a large number of technologies available, systematic synthesis of a sustainable integrated biorefinery which simultaneously considers economic performance, environmental impact, and energy requirement is a challenging task. To address this issue, multiobjective optimization approaches are used in this work to synthesize a sustainable integrated biorefinery. In addition, a novel approach (incremental environmental burden) to assess the environmental impact for an integrated biorefinery is presented. To illustrate the proposed approach, a palm‐based biomass case study is solved. © 2014 American Institute of Chemical Engineers AIChE J, 61: 132–146, 2015     

A generalized method for the synthesis and design of reactive distillation columns

Owing to the combination between the reaction operation and the separation operation involved, it is extremely difficult to determine in advance the optimum configuration of a reactive distillation column and this makes process synthesis and design a great challenging task. Currently, no easy-to-use and yet effective methods are available to guide process synthesis and design, restricting considerably the applications and therefore the impacts of reactive distillation columns to the chemical process industry. In this paper, a generalized method is proposed for the synthesis and design of reactive distillation columns in terms of the insights from process intensification. The method is initiated from a simple process design with all feeds of reactants at the middle of the process and all stages as reactive ones. In terms of an economical objective function, it can be evolved into the optimum process design via sequential structure adjustments, including reactive section arrangement, feed stage relocation, feed splitting, and catalyst redistribution. The generalized method proposed is characterized by great simplicity in principle, the capability to tap the full potentials of process intensification, and the high robustness to the initial guess of process configuration as well as the thermodynamic properties of the reacting mixtures separated. Four example systems are employed to evaluate the generalized method proposed and the obtained outcomes demonstrate its effectiveness and applicability to the synthesis and design of various reactive distillation columns.     

一种通用的反应蒸馏塔综合与设计方法

反应精馏是反应过程和分离过程耦合为一体的单元操作,已成为当今研究的重要领域。然而,到目前为止并没有一套通用简便的方法去指导反应精馏过程的综合与设计,严重限制了它的广泛应用。本文在过程强化原理的基础上提出了一种反应蒸馏塔通用的综合与设计方法,并利用2种反应蒸馏系统来评价所提出的设计策略。结果表明,该综合设计方法可以简便高效地搜索出反应蒸馏塔的最优结构,适用于不同类型的反应蒸馏塔的综合与设计。     

集成反应分离耦合控制建模方法及其在甘露醇制备上的应用

针对集成反应分离耦合控制中存在最佳耦合点难以确定的技术难点,自主提出了一种能将反应分离进行耦合控制的建模方法。其基于电化学还原反应与连续色谱分离原理,通过将电化学反应方程的浓度值输出作为连续色谱分离传质方程的初始边界条件,耦合两者的传质方程进行联立求解,能够揭示电化学反应时间控制与产物转化率以及分离效果等之间的作用关系。将该方法应用于甘露醇的制备过程,其实验与模拟结果对比表明,两者吻合较好。该建模方法具有较强的预测能力,不仅获得了集成反应分离制备甘露醇的最佳耦合控制点,而且方法具有一定的推广应用价值。