Experiences from using heat integration software to determine retrofit opportunities within a refinery process

Heat integration techniques can be used to optimize the energy requirement for both new and retrofit plant designs. Software tools for identifying retrofit options are becoming available. This paper reports our experiences from using heat exchanger network (HEN) optimization software for a retrofit case study of an oil refinery process. The HEN optimization software was used to automate the search for the most beneficial retrofit designs following the twostage process proposed by Asante and Zhu. The software provided three potential retrofit designs. Results from this analysis were used as the basis of a rigorous mass and energy balance simulation of the plant. The simulation corroborated the energy savings, but there were some important differences. The simulation required 20% more heat exchange area. Furthermore, the retrofit design involving one topology change was shown to be less economic than an alternative design. These differences are discussed and a revised methodology is proposed.     

On the costs of parameter uncertainties. Part 2: Impact of EVOP procedures on the optimization and design of experiments

Pinto (1998) showed that an economical value might be assigned to model parameter uncertainties, which could be used for process optimization and for taking decisions during sequential experimental designs. The procedure is extended to take into account the fact that plant operation conditions change with time, leading to improvements of the plant operation and allowing the partial recovering of losses introduced by uncertain parameters during plant design. It is shown that the use of EVOP (Evolutionary Operation Procedures) during plant operation may reduce the cost of parameter uncertainties, exerting a major impact on process optimization and optimal design of experiments.     

Synthesis of optimal energy recovery networks using discrete methods

A flexible modular program system for the synthesis of optimal energy recovery networks is described. Process networks are generated to meet both stream temperature and pressure specifications. The system makes use of a price based decomposition algorithm for sub-process integration. It incorporates heuristic decision making and employs a “branch and bound” combinatorial technique for optimization of network configuration. The ability of the system to generate very realistic process designs is demonstrated by its application to the design of an energy recovery system for an ethylene plant with its associated refrigeration facility.     

Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach

Corporate approaches to improve environmental performance cannot be undertaken in isolation, so a concerted effort along the supply chain (SC) entities is needed which poses another important challenge to managers. This work addresses the optimization of SC planning and design considering economical and environmental issues. The strategic decisions considered in the model are facility location, processing technology selection and production–distribution planning. A life cycle assessment (LCA) approach is envisaged to incorporate the environmental aspects of the model. IMPACT 2002+ methodology is selected to perform the impact assessment within the SC thus providing a feasible implementation of a combined midpoint–endpoint evaluation. The proposed approach reduces the value-subjectivity inherent to the assignment of weights in the calculation of an overall environmental impact by considering endpoint damage categories as objective function. Additionally, the model performs an impact mapping along the comprising SC nodes and activities. Such mapping allows to focus financial efforts to reduce environmental burdens to the most promising subjects. Furthermore, consideration of CO₂ trading scheme and temporal distribution of environmental interventions are also included with the intention of providing a tool that may be utilized to evaluate current regulatory policies or pursue more effective ones. The mathematical formulation of this problem becomes a multi-objective MILP (moMILP). Criteria selected for the objective function are damage categories impacts, overall impact factor and net present value (NPV). Main advantages of this model are highlighted through a realistic case study of maleic anhydride SC production and distribution network.     

A LCA Based Biofuel Supply Chain Analysis Framework

This paper presents a life cycle assessment （LCA） based biofuel supply chain （SC） analysis framework which enables the study of economic, energy and environmental （3E） performances by using multi-objective opti-mization. The economic objective is measured by the total annual profit, the energy objective is measured by the average fossil energy （FE） inputs per MJ biofuel and the environmental objective is measured by greenhouse gas （GHG） emissions per MJ biofuel. A multi-objective linear fractional programming （MOLFP） model with multi-conversion pathways is formulated based on the framework and is solved by using theε-constraint method. The MOLFP prob-lem is turned into a mixed integer linear programming （MILP） problem by setting up the total annual profit as the optimization objective and the average FE inputs per MJ biofuel and GHG emissions per MJ biofuel as constraints. In the case study, this model is used to design an experimental biofuel supply chain in China. A set of the weekly Pareto optimal solutions is obtained. Each non-inferior solution indicates the optimal locations and the amount of biomass produced, locations and capacities of conversion factories, locations and amount of biofuel being supplied in final markets and the flow of mass through the supply chain network （SCN）. As the model reveals trade-offs among 3E criteria, we think the framework can be a good support tool of decision for the design of biofuel SC.     

Distilled water production using geothermally heated seawater

The basic technical and economic issues related to the production of distilled water from geothermally heated waters in coastal areas of Baja California, Mexico, are compiled and reviewed. A mathematical model that takes into consideration water temperature, production flow rate and plant design is presented. The use of colder waters in the condenser that results in increased production rates and reduced initial investments is studied, as well as different possible plant designs. It is shown that in coastal areas fresh water produced from this type of low-enthalpy geothermal resources can be cost effective.     

燃料乙醇温室气体排放与能量投入/产出的探讨

介绍了基于全生命周期评价和生物燃料系统分析模型的燃料乙醇温室气体排放及能量投入/产出研究结果,总结相关研究工作进展。从降低温室气体排放和能量投入/产出比的角度,对燃料乙醇原料的选择、生产工艺优化的指导原则、能源产品多元化以及工厂布局与物流等问题提出相应建议。     

Optimal biocompatible solvent design for a two-stage extractive fermentation process with cell recycling

In this study, crisp and fuzzy multiple-goal optimization approaches are respectively introduced to design an optimal biocompatible solvent to a two-stage extractive fermentation with cell recycling for ethanol production. When designing a biocompatible solvent for the extractive fermentation process, many issues, such as extractive efficiency, conversion, amount of solvent utilized and so on, have to be considered. An interactive multiple-goal design procedure is introduced to determine a trade-off result in order to satisfy such contradicted goals. Both approaches could be iterated to solve the interactive multiple-goal design problem in order to yield a trade-off result. However, the crisp optimization design is a tedious task that requires the designer to provide various pairs of the upper bounds for the design problem to obtain the corresponding solution. The fuzzy optimization approach is able to be trade-off several goals simultaneously and to yield the overall satisfactory grade for the product/process design problem.     

A multi‐objective optimization approach to polygeneration energy systems design

Polygeneration, typically involving co‐production of methanol and electricity, is a promising energy conversion technology which provides opportunities for high energy utilization efficiency and low/zero emissions. The optimal design of such a complex, large‐scale and highly nonlinear process system poses significant challenges. In this article, we present a multiobjective optimization model for the optimal design of a methanol/electricity polygeneration plant. Economic and environmental criteria are simultaneously optimized over a superstructure capturing a number of possible combinations of technologies and types of equipment. Aggregated models are considered, including a detailed methanol synthesis step with chemical kinetics and phase equilibrium considerations. The resulting model is formulated as a non‐convex mixed‐integer nonlinear programming problem. Global optimization and parallel computation techniques are employed to generate an optimal Pareto frontier. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Optimal Temperature and pH Control for a Batch Simultaneous Saccharification and Co-Fermentation Process

Crisp and fuzzy optimization approaches were applied to design an optimal temperature and pH control policy for a batch process of simultaneous saccharification and co-fermentation (SSCF) for ethanol production from lignocellulose, using the enzyme and recombinant strain Zymomonas mobilis ZM (pZB5). To determine an optimal temperature and pH control policy, we applied the Arrhenius relationship to each rate constant to express the temperature and pH effects in the kinetic model for both saccharification and fermentation. The goal of the optimal design was to determine the optimal temperature, pH value, initial lignocellulosic concentration, and fermentation time for maximizing the ethanol productivity under the constraints of the follow-up separation specifications. The interactive crisp and fuzzy optimization methods were applied to solve the trade-off optimization problems for obtaining a compromised design. The fuzzy goal attainment approach obtained a compromised design more flexibly than did the crisp optimization. We also compared the performances for batch and fed-batch SSCF, and used various composition proportions for the batch SSCF to determine a series of optimal designs for the fuzzy goal attainment problem. Batch SSCF was slightly more effective than fed-batch fermentation, and spruce exhibited the maximum productivity because of its higher cellulose and lower hemicellulose contents compared with those of other sources.     

Flexible design‐planning of supply chain networks

Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This work focuses on the design and retrofit of SCs. Traditional approaches available in literature addressing this problem usually utilize as departing point a rigid predefined network structure which may restrict the opportunities of adding business value. Instead, a novel flexible formulation approach which translates a recipe representation to the SC environment is proposed to solve the challenging design‐planning problem of SC networks. The resulting mixed integer linear programming model is aimed to achieve the best NPV as key performance metric. The potential of the presented approach is highlighted through illustrative examples of increasing complexity, where results of traditional rigid approaches and those offered by the flexible framework are compared. The implications of exploiting this potential flexibility to improve the SC performance are highlighted and are the subject of our further research work. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

A design procedure for heat-integrated distillation column sequencing of natural gas liquid fractionation processes

The separation of NGL (natural gas liquids) in gas processing is energy-intensive, requiring systematic process design and optimization to reduce energy consumption and to identify cost-effective solutions for the recovery valuable hydrocarbons. As NGL fractionation processes require a sequence of distillation columns to separate multi-component mixtures the determination of optimal energy-efficient distillation sequences and operating conditions is not a simple task. A design methodology is proposed in this study in which the process simulator Aspen HYSYS^® is linked with an optimization algorithm available in MATLAB^®. The proposed methodology involves a procedure where in the first step possible distillation sequences are screened using a short-cut distillation column model. In the second step a few selected and promising candidate distillation sequences are further simulated and optimized, again using the same short-cut model. Finally, rigorous simulations are used to validate and confirm the feasibility of the optimal designs. A case study is presented to demonstrate the applicability of the proposed design framework for the design and optimization of NGL fractionation processes in practice.     

Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty

In this paper, we address the strategic planning of integrated bioethanol–sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.     

Sequential experimental design based on multiobjective optimization procedures

Model-based sequential experimental designs are frequently applied for discrimination of rival models and/or estimation of precise model parameters. Although the development and use of a single design criterion to perform the simultaneous model discrimination and precise parameter estimation seem appealing, published material indicates that previous attempts to develop such a single design criterion have not been successful. Despite that, this problem has rarely been analyzed with the help of multiobjective optimization procedures. In this work, a multiobjective optimization method based on the particle swarm optimization procedure is used to build the Pareto fronts in experimental design problems where distinct design criteria used for discrimination of rival models and/or estimation of precise model parameters are considered simultaneously. It is shown through the rigorous analysis of the Pareto sets that both design objectives are frequently conflicting, which means that optimum discrimination of rival models and estimation of precise model parameters cannot be performed simultaneously in many cases. However, it is also shown that the use of the posterior covariance matrix of estimated model parameters for model discrimination makes the design of experiments for the simultaneous optimum model discrimination and estimation of model parameters possible in many experimental design problems.     

Assessing plant design with regard to MPC performance

Model Predictive Control is ubiquitous in the chemical industry and offers great advantages over traditional controllers. Notwithstanding, new plants are being projected without taking into account how design choices affect the MPC’s ability to deliver better control and optimization. Thus a methodology to determine if a certain design option favours or hinders MPC performance would be desirable. This paper presents the economic MPC optimization index whose intended use is to provide a procedure to compare different designs for a given process, assessing how well they can be controlled and optimised by a zone constrained MPC. The index quantifies the economic benefits available and how well the plant performs under MPC control given the plant’s controllability properties, requirements and restrictions. The index provides a monetization measure of expected control performance.This approach assumes the availability of a linear state-space model valid within the control zone defined by the upper and lower bounds of each controlled and manipulated variable. We have used a model derived from simulation step tests as a practical way to use the method. The impact of model uncertainty on the methodology is discussed. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of a MPC to reach dynamic equilibrium near process restrictions, which in turn increases product quality giveaway and costs. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the applicability of the index.     

Transition from Batch to Continuous Operation in Bio‐Reactors: A Model Predictive Control Approach and Application

This work considers the problem of determining the transition of ethanol‐producing bio‐reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov‐based non‐linear model predictive controller is utilized that stabilizes the bio‐reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed‐loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro‐organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio‐reactor.     

多原料生物质燃料乙醇生产系统优化

以利用小麦、玉米、木薯等多种原料生产燃料乙醇的系统为研究对象,针对存在的原料成本、产品价格、补贴政策、化石能源市场等诸多因素不断变化影响效益的问题,从原料结构优化、工艺参数优化、生产方案选择等关键角度,建立了燃料乙醇生产的全流程优化非线性规划模型,提出了影响系统效益的三个主要问题. 原料结构优化结果表明,经模型优化后系统单月利润平均可增加5%~8%;在某一原料如小麦价格不断上涨时,可在满足生产工艺和产品品质要求的前提下用其他低价原料如木薯代替. 产品路线优化结果表明,沼气价格从0.6 ￥/m3逐步增加时,企业应根据价格变化逐步调整各产品产量,以保证整体效益最大化,超过1.3 ￥/m3时,则没有必要再利用沼气发电,可直接销售. 该模型有助于解决实际生产中的决策问题及多原料多产品的生物质能源企业提高整体效益、合理安排生产.     

超临界CO2和植物油脱臭馏出物相平衡研究

研究了植物油脱臭馏出物在超临界CO2中的相平衡问题。基于组分的相似性把含有复杂组分的植物油脱臭馏出物简化为两种虚拟组分,用改进的混合规则和超临界组分的能量参数计算式,结合PR状态方程进行计算：理论计算与实验数据相吻合,为间歇式或连续逆流式超临界CO2萃取分离的设计提供了依据。     

Implementation of Ethanol Dehydration Using Dividing-Wall Heterogeneous Azeotropic Distillation Column

In this article, the design and optimization procedures of a dividing-wall column for heterogeneous azeotropic distillation (DWC-A) using cyclohexane as an entrainer for ethanol dehydration are investigated. The proposed procedures can detect the optimal values of the design variables and thereby guarantee the minimum energy requirements, which is related to the minimum CO₂ emissions and the lowest total annual cost (TAC). Since ethanol and water form an azeotrope under atmosphere pressure, a conventional heterogeneous azeotropic distillation sequence (CHADS), including an azeotropic column and a recovery column, is usually used to perform the ethanol dehydration process. However, due to high energy requirements and equipment investments of CHADS, the TAC is at a relatively high level. DWC-A can be used to eliminate the condenser of the second column and decrease the degree of back-mixing. Both CHADS and DWC-A are simulated with Aspen Plus®, and the results show that DWC-A has an energy saving of 42.17% and the TAC reduction of 35.18% along with higher thermodynamic efficiency and reduction in greenhouse gas emissions.     

Heat exchanger network synthesis with detailed exchanger designs—2. Hybrid optimization strategy for synthesis of heat exchanger networks

We propose a new strategy to synthesize heat exchanger networks with detailed designs of individual heat exchangers. The proposed strategy uses a multistep approach by first obtaining a heat exchanger network topology through solving a modified version of the mixed integer nonlinear programming (MINLP) stage-wise superstructure of Yee and Grossmann, which includes a smoothed LMTD approximation and pressure drops. In a second nonlinear programming (NLP) suboptimization step, we allow for nonisothermal mixing to solve problems with or without exchanger bypasses. The selected heat exchangers along with the mass and energy balances obtained are then used to design the network with detailed exchanger designs through solving a sequence of NLPs for individual heat exchanger designs. The NLPs are based on the detailed discretized optimization models of Kazi et al., which solve quickly and reliably to obtain heat exchangers based on rigorous, first-principles derived coupled differential equations. These models solve a differential algebraic equation system and do not rely on usual assumptions associated with other heuristic-based exchanger design methods, such as log mean temperature difference and F_T correction factors. These detailed exchanger designs are then used to update the network optimization model through sets of correction factors on heat exchanger area, number of shells, heat transfer coefficients, and pressure drops of each exchanger design, in a method based on that of Short et al. The method solves reliably, guaranteeing feasible exchangers for every potential network generated by the shortcut models, through validation with rigorous heat exchanger models at every iteration. In addition, the method does not increase the nonlinearity of the MINLP model, nor does it require any manual intervention or initialization from the user. Three examples are solved and the results are compared to those obtained in the literature.