Optimal design and planning of sustainable chemical supply chains under uncertainty

This article addresses the design of sustainable chemical supply chains in the presence of uncertainty in the life cycle inventory associated with the network operation. The design task is mathematically formulated as a bi‐criterion stochastic mixed‐integer nonlinear program (MINLP) that simultaneously accounts for the maximization of the net present value and the minimization of the environmental impact for a given probability level. The environmental performance is measured through the Eco‐indicator 99, which incorporates the recent advances made in Life Cycle Assessment. The stochastic model is converted into its deterministic equivalent by reformulating the probabilistic constraint required to calculate the environmental impact in the space of uncertain parameters. The resulting deterministic bi‐criterion MINLP problem is further reformulated as a parametric MINLP, which is solved by decomposing it into two sub‐problems and iterating between them. The capabilities of the proposed model and solution procedure are illustrated through two case studies for which the set of Pareto optimal, or efficient solutions that trade‐off environmental impact and profit, are calculated. These solutions provide valuable insights into the design problem and are intended to guide the decision maker towards the adoption of more sustainable design alternatives. © 2008 American Institute of Chemical Engineers AIChE J, 2009     

Balancing responsiveness and economics in process supply chain design with multi‐echelon stochastic inventory

This article is concerned with the optimal design of multi‐echelon process supply chains (PSCs) under economic and responsive criteria with considerations of inventory management and demand uncertainty. The multi‐echelon inventory systems are modeled with the guaranteed service approach to handle the uncertain demands at each echelon. The maximum guaranteed service time of the last echelon of the PSC is proposed as a measure of a PSC's responsiveness. The problem is formulated as a bi‐criterion mixed‐integer nonlinear program (MINLP) with the objectives of minimizing the annualized cost (economic objective) and minimizing the maximum guaranteed service times of the markets (responsiveness objective). The model simultaneously predicts the optimal network structure, transportation amounts, and inventory levels under different specifications of the PSC responsiveness. An example on acetic acid supply chain is presented to illustrate the application of the proposed model and to comprehensively compare different measures of PSC responsiveness. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

On optimal sensing and actuation design for an industrial scale steam methane reformer furnace

The spatial temperature distribution in the highly energy‐intensive furnace unit in a steam reforming‐based hydrogen manufacturing plant determines the energy efficiency of the plant. While the fuel distribution among the burners can be manipulated to control the furnace temperature distribution, adequate temperature measurements is a prerequisite. Typical furnaces have hundreds of tubes and burners, and economic considerations dictate that the number of temperature sensors and flow actuators required for automatic temperature optimization be minimized. In this article, we investigate several formulations for the design of the optimal sensor and actuation configurations for an industrial furnace. We initially formulate the optimal sensor placement problem as a bi‐level optimization problem, and exploit the problem structure to obtain an equivalent mixed‐integer linear program formulation. We then provide an extension to the combined sensor and actuator placement. We demonstrate the efficacy of our approach through simulation case studies based on industrial data. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3225–3237, 2016     

Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis

This article addresses the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. The economic objective is measured by the total annualized cost, the environmental objective is measured by the life cycle greenhouse gas emissions, and the social objective is measured by the number of accrued local jobs. A multiobjective mixed‐integer linear programming (mo‐MILP) model is developed that accounts for major characteristics of cellulosic ethanol supply chains, including supply seasonality and geographical diversity, biomass degradation, feedstock density, diverse conversion pathways and byproducts, infrastructure compatibility, demand distribution, regional economy, and government incentives. Aspen Plus models for biorefineries with different feedstocks and conversion pathways are built to provide detailed techno‐economic and emission analysis results for the mo‐MILP model, which simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The mo‐MILP problem is solved with an ε‐constraint method; and the resulting Pareto‐optimal curves reveal the tradeoff between the economic, environmental, and social dimensions of the sustainable biofuel supply chains. The proposed approach is illustrated through two case studies for the state of Illinois. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Sustainable process design and synthesis of hydrocarbon biorefinery through fast pyrolysis and hydroprocessing

The process design and synthesis of hydrocarbon biorefinery, which is composed of fast pyrolysis, biocrude collection, hydroprocessing and hydrogen production sections, under economic and environmental considerations are concerned. A superstructure is developed that includes multiple process alternatives in each stage of the process flow diagram. A bi‐criteria mixed integer nonlinear programming model is proposed to maximize the economic performance measured by the net present value and minimize the global warming potential according to life cycle assessment procedures. The bi‐criteria mixed integer nonlinear programming model is solved with the ε‐constraint method, and the resulting Pareto curve reveals the trade‐off between the economic and environmental performance of the process. The two selected “good choice” optimal designs indicate net present values of 573 and 93.6 $MM (unit costs of $3.43 and $5.26 per gallon of gasoline equivalent), corresponding to global warming potentials of 100 and 53 kton CO₂ equivalent per year (unit greenhouse emissions of 1.95 and 2.04 kg CO₂ per gallon of gasoline equivalent), respectively. © 2014 American Institute of Chemical Engineers AIChE J, 60: 980–994, 2014     

炼油厂氢气网络建模与多目标优化(英文)

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.     

集成轻烃回收单元代理模型的氢气网络多目标优化

当前炼油企业氢气需求持续增长,导致炼厂成本及生产过程温室气体排放增加,炼油企业通过增设轻烃回收单元对氢气和轻烃组分进行回收利用,能有效缓解这一现状。因此,在氢气网络优化中有必要考虑轻烃回收单元。本研究提出了一种集成轻烃回收单元的氢气网络多目标数学规划模型,对轻烃回收单元采用代理模型建模方法,解决了直接嵌入严格机理模型可能导致的高计算成本问题,以总年度费用最小为优化目标,同时将系统的环境影响也纳入优化目标。实例计算表明,所提出的方法能够有效降低氢气网络的年度费用及温室气体排放,并揭示了集成轻烃回收单元的氢气网络经济性能与环境影响之间的权衡关系,为工业应用提供了一定的理论基础。     

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.     

考虑环境影响的间歇过程多目标最优化设计

同时考虑费用和环境影响的间歇化工过程多目标最优化设计问题的求解,通常的做法是使用权重系数法,将其转变成单一目标来优化。但大多数情况下,这种权重系数很难确知。因此,有必要提供多个解以便于设计者作出合理的最终选择。采用多目标遗传算法和线性规划相结合的方法求解出间歇化工过程优化设计模型的非劣解集,并与不同权重系数下的单目标算例进行了比较。结果表明,用多目标遗传算法求解间歇化工过程是有效的。这为设计者在间歇化工过程最优化研究考虑环境因素的决策提供了更多的选择。     

Optimal processing network design under uncertainty for producing fuels and value‐added bioproducts from microalgae: Two‐stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm

Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large‐scale processing network for producing a variety of algae‐based fuels and value‐added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two‐stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off‐the‐shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column‐and‐constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly‐3‐hydroxybutyrate as the final fuel and bioproduct, respectively. © 2016 American Institute of Chemical Engineers AIChE J, 63: 582–600, 2017     

Optimization of refinery hydrogen network based on chance constrained programming

Deterministic optimization approaches have been developed and used in the optimization of hydrogen network in refinery. However, uncertainties may have a large impact on the optimization of hydrogen network. Thus the consideration of uncertainties in optimization approaches is necessary for the optimization of hydrogen network. A novel chance constrained programming (CCP) approach for the optimization of hydrogen network in refinery under uncertainties is proposed. The stochastic properties of the uncertainties are explicitly considered in the problem formulation in which some input and state constraints are to be complied with predefined probability levels. The problem is then transformed to an equivalent deterministic mixed-integer nonlinear programming (MINLP) problem so that it can be solved by a MINLP solver. The solution of the optimization problem provides comprehensive information on the economic benefit under different confidence levels by satisfying process constraints. Based on this approach, an optimal and reliable decision can be made, and a suitable compensation between the profit and the probability of constraints violation can be achieved. The approach proposed in this paper makes better use of resources and can provide significant environmental and economic benefits. Finally, a case study from a refinery in China is presented to illustrate the applicability and efficiency of the developed approach.     

以(火用)最小为目标的氢气分配网络优化

优化氢气分配网络,合理利用氢气资源,对炼化企业的节能降耗、降低生产成本具有重要意义。在氢气分配网络中,除了氢气的消耗,最主要的能耗就是压缩气体时所消耗的功。为了将氢气的消耗和压缩功的消耗从节约能源的角度统一优化,提出以“火用基准”为基础对氢气分配网络模型进行优化设计,即将新氢用量和压缩过程的功耗都转换成火用耗,以总的火用消耗量作为衡量氢网络优劣的基准。同时利用网络结构调优策略,以有效地减少压缩机的数目,保证了系统的能量性能和经济效益。通过一个精炼厂实例的成功应用,证实了此种方法的有效性。     

A superstructure‐based mixed‐integer programming approach to optimal design of pipeline network for large‐scale CO2 transport

Pipeline transport is the major means for large‐scale and long‐distance CO₂ transport in a CO₂ capture and sequestration (CCS) project. But optimal design of the pipeline network remains a challenging problem, especially when considering allocation of intermediate sites, like pump stations, and selection of pipeline routes. A superstructure‐based mixed‐integer programming approach for optimal design of the pipeline network, targeting on minimizing the overall cost in a CCS project is presented. A decomposition algorithm to solve the computational difficulty caused by the large size and nonlinear nature of a real‐life design problem is also presented. To illustrate the capability of our models. A real‐life case study in North China, with 45 emissions sources and four storage sinks, is provided. The result shows that our model and decomposition algorithm is a practical and cost‐effective method for pipeline networks design. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2442–2461, 2014     

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     

A case study for reactor network synthesis: the vinyl chloride process

A key objective of the integrated reactor network synthesis approach is the development of waste minimizing process flowsheets (Lakshmanan & Biegler, 1995). With increasing environmental concerns in process design, there is a particularly strong need to maximize conversion to product and avoid generation of wasteful byproducts within the reactor network. This also avoids expensive treatment and separation costs downstream in the process. In this study, we present an application of the mixed integer nonlinear programming (MINLP)-based reactor network synthesis strategy developed by Lakshmanan and Biegler (1996a). Here we focus on applying these reactor network synthesis concepts to the vinyl chloride monomer production process. Vinyl chloride is currently produced by a balanced production process from ethylene, chlorine and oxygen with three separate reaction sections: oxychlorination of ethylene; direct chlorination of ethylene; and pyrolysis of ethylene dichloride. The hydrogen chloride produced in the pyrolysis reactor is used completely in the oxychlorination reactor. Byproducts such as chlorinated hydrocarbons and carbon oxides are generated by these reaction sections. These are studied using reaction kinetic models for the three reaction sections. The case study results in optimal reactor networks that improve the conversion of ethylene to vinyl chloride and minimize the formation of byproducts. These results are used to generate an improved flowsheet for the production of vinyl chloride monomer. Moreover, an overall profit maximization, that includes the effect of heat integration, is presented and a set of recommendations that improve the selectivity of vinyl chloride production are outlined. Finally, the optimal reactor structures, overall conversion and annual profit are shown to be only mildly sensitive with respect to small changes in the kinetic parameters.     

Sustainable design and synthesis of algae‐based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration

The superstructure optimization of algae‐based hydrocarbon biorefinery with sequestration of CO₂ from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed‐integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two‐stage heuristic solution algorithm to solve the nonconvex MINLP model. Finally, the bicriteria optimization problem is solved with ε‐constraint method, and the resulting Pareto‐optimal curve reveals the trade‐off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO₂‐eq per gallon. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1599–1621, 2013     

Analysis of the Relation between Coupled Sink and Purification Based on Hydrogen Network Integration

In a hydrogen network, sinks and sources are generally connected to reactors or purifiers, which affect their compositions and flow rates. The relationship between these streams is studied based on the integration of the hydrogen network to identify the feasible and optimal operating conditions of reactors and purifiers. Equations are deduced to describe the quantitative relationship between hydrogen consumption, hydrogen concentration, flow rates of coupled sink and source, purification feed, and purified product. The purification and hydrogen‐consuming reactor parameters can be optimized in the design and operation stage of a hydrogen network. The case study proves that the proposed method is simple, easy to understand, and can be applied to identify the variation trend line and feasible region accurately without tedious calculation.     

Optimization of district heating systems based on the demand forecast in the capital region

A district heating system (DHS) consists of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the region. DHS has taken charge of an increasingly important role as the energy cost increases recently. In this work, a model for operational optimization of the DHS in the metropolitan area is presented by incorporating forecast for demand from customers. In the model, production and demand of heat in the region of Suseo near Seoul, Korea, are taken into account as well as forecast for demand using the artificial neural network. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS. The solution gives the optimal amount of network transmission and supply cost. The optimization system coupled with forecast capability can be effectively used as design and longterm operation guidelines for regional energy policies.     

Signal Extraction for Non‐Stationary Multivariate Time Series with Illustrations for Trend Inflation

This article advances the theory and methodology of signal extraction by developing the optimal treatment of difference stationary multivariate time‐series models. Using a flexible time‐series structure that includes co‐integrated processes, we derive and prove formulas for minimum mean square error estimation of signal vectors in multiple series, from both a finite sample and a bi‐infinite sample. As an illustration, we present econometric measures of the trend in total inflation that make optimal use of the signal content in core inflation.     

Computer‐Aided Molecular Design of Environmentally Friendly Solvents for Separation Processes

This review paper presents an optimization technology for the computer‐aided molecular design of environmentally friendly solvents. The approach combines a stochastic optimization method and group contribution methods (GC‐methods) to design solvents with desirable physicochemical and environmental properties. A simulated annealing algorithm is used to investigate feasible molecular structures. The main objective method is adopted to balance the multi‐objective functions. One property is chosen as the main objective function, while the other properties are considered as constraints, and thus, the multi‐objective problem is transformed into a single objective one. The optimal solution is a set of molecules satisfying the formulated target. The properties of each molecule are evaluated through GC‐methods, including pure component properties, mixture properties and environmentally properties. Finally, the proposed methodology is illustrated with several examples of industrial separations.