Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

Multiobjective design of interplant trigeneration systems

A systematic approach for heat integration into an eco‐industrial park through an integrated trigeneration system is presented. The approach is based on a new superstructure formulated as a multiobjective mixed‐integer nonlinear programming model, where intraplant and interplant heat exchange for the process streams is allowed, in addition to the energy integration into the utility system that is constituted by a steam Rankine cycle (to produce electric power and hot utility), an organic Rankine cycle (to recover waste heat and produce electric power), and an absorption refrigeration cycle (to recover waste heat and provide refrigeration). To run the utility system, several external heat sources (solar, fossil fuels, and biofuels) are considered, which impact the economic, environmental, and social objectives considered in the model. A systematic approach to tradeoff the objectives considered is presented. Two examples are presented, where the advantages of the integrated eco‐industrial park are shown. © 2013 American Institute of Chemical Engineers AIChE J, 60: 213–236, 2014     

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     

两种不确定条件下工业园区的优化组合

生态工业和生态工业园区是近年来出现的解决环境问题和可持续发展的新思路 .对不确定条件下工业园区优化组合问题的研究是其中一个非常重要的组成部分 .提出了当同时存在周期变动和随机变动两种不同类型的不确定变量时 ,工业园区优化问题的求解方法及适用范围 .应用该法对一工业园区案例进行了计算和分析 ,表明了该法的有效性 .     

Environmental life cycle assessment of a pre‐fragmented high explosive grenade

Organisations today face increasing environmental constraints, e.g. in the form of legal and customer requirements; the defence sector is no exception. There is a need to evaluate and limit environmental effects of defence activities and materiel. In this study we used quantitative Life Cycle Assessment (LCA) and a method for simplified LCA (the Material, Energy, Chemicals and Others (MECO) method) to assess the environmental impacts of a grenade. The aims of the study are to identify aspects in the grenade's life‐cycle that have the largest environmental impact, suggest improvement possibilities, make a comparison between different approaches for waste management of munitions, and to perform a demonstrative case for the application of LCA to munitions. Significant environmental aspects of the grenade's life‐cycle include use of metals, use of fossil fuels, and detonation outdoors. The study shows that an LCA can be used to analyse environmental impacts from munitions. The simplified LCA gave information that is complementary to the quantitative LCA. Copyright © 2005 Society of Chemical Industry     

Multiobjective optimization of an industrial grinding operation under uncertainty

Multiobjective optimization of an industrial grinding operation under various parameter uncertainties is carried out in this work. Two sources of uncertainties considered here are related to the (i) parameters that are used inside a model representing the process under consideration and subjected to experimental and regression errors and (ii) parameters that express operators’ choice for assigning bounds in the constraints and operators prefer them to be expressed around some value rather than certain crisp value. Uncertainty propagation of these parameters through nonlinear model equations is reflected in terms of system constraints and objectives that are treated here using chance constrained fuzzy simulation based approach. Such problems are treated in literature using the standard two stage stochastic programming methodology that has a drawback of leading to combinatorial explosion with an increase in the number of uncertain parameters. This problem is overcome here using a combination of fuzzy and chance constrained programming approach that tackles the problem by representing and treating the uncertain parameters in a different manner. Simultaneous maximization of grinding circuit throughput and percent passing mid size fraction are studied here with upper bound constraints for various performance metrics for the grinding circuit, e.g. percent passing of fine and coarse size classes, percent solids in the grinding circuit final outlet stream and circulation load of the grinding circuit. Uncertain parameters considered are grindability indices of rod mill and ball mill, sharpness indices of primary and secondary cyclones and the respective upper bounds for the constraints mentioned above. The deterministic multiobjective grinding optimization model of Mitra and Gopinath [2004. Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm. Chem. Eng. Sci. 59, 385-396.] forms the basis of this work on which various effects of uncertain parameters are shown and analyzed in a Pareto fashion. Nondominated sorting genetic algorithm, NSGA II, a popular elitist evolutionary multiobjective optimization approach, is used for this purpose.     

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 computational framework and solution algorithms for two‐stage adaptive robust scheduling of batch manufacturing processes under uncertainty

A novel two‐stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed‐integer linear programming model of batch scheduling into a two‐stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the Benders decomposition algorithm and the column‐and‐constraint generation (C&CG) algorithm to efficiently solve the resulting two‐stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the Benders decomposition algorithm, and the proposed two‐stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling. © 2015 American Institute of Chemical Engineers AIChE J, 62: 687–703, 2016     

Design under uncertainty using parallel multiperiod dynamic optimization

A technique for optimizing dynamic systems under uncertainty using a parallel programming implementation is developed in this article. A multiple‐shooting discretization scheme is applied, whereby each shooting interval is solved using an error‐controlled differential equation solver. In addition, the uncertain parameter space is discretized, resulting in a multiperiod optimization formulation. Each shooting interval and period (scenario) realization is completely independent, thus a major focus of this article is on demonstrating potential computational performance improvement when the embedded dynamic model solution of the multiperiod algorithm is implemented in parallel. We assess our parallel multiperiod and multiple‐shooting‐based dynamic optimization algorithm on two case studies involving integrated plant and control system design, where the objective is to simultaneously determine the size of the process equipment and the control system tuning parameters that minimize cost, subject to uncertainty in the disturbance inputs. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3151–3168, 2014     

Sustainable process design by the process to planet framework

Sustainable process design (SPD) problems combine a process design problem with life cycle assessment (LCA) to optimize process economics and life cycle environmental impacts. While SPD makes use of recent advances in process systems engineering and optimization, its use of LCA has stagnated. Currently, only process LCA is utilized in SPD, resulting in designs based on incomplete and potentially inaccurate life cycle information. To address these shortcomings, the multiscale process to planet (P2P) modeling framework is applied to formulate and solve the SPD problem. The P2P framework offers a more comprehensive analysis boundary than conventional SPD and greater modeling detail than advanced LCA methodologies. Benefits of applying this framework to SPD are demonstrated with an ethanol process design case study. Results show that current methods shift emissions outside the analysis boundary, while applying the P2P modeling framework results in environmentally superior process designs. Future extensions of the P2P framework are discussed. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3320–3331, 2015     

Supply chain management using an optimization driven simulation approach

In this work, we propose a hybrid simulation‐based optimization framework to solve the supply chain management problem. The hybrid approach combines a mathematical programming model with an agent‐based simulation model and uses them in an iterative framework. The optimization model is used to guide the decisions toward an optimal allocation of resources given the realistic supply chain representation given by the simulation. Thus, the proposed approach provides a more realistic solution compared to a stand‐alone optimization model, often a simplified representation of the actual system, by making use of the simulation model, which captures the detailed dynamic behavior of the system. A multiobjective problem has been formulated by taking into consideration the environmental impact of supply chain operations. The proposed framework has been applied to small‐scale case studies to study the effectiveness of the approach for such problems. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4612–4626, 2013     

Minimization of the nonrenewable energy consumption in bioethanol production processes using a solar‐assisted steam generation system

The multiobjective optimization of a corn‐based bioethanol plant coupled with a solar‐assisted steam generation system with heat storage is described. Our approach relies on the combined use of process simulation, rigorous optimization tools, and economic and energetic plant analysis. The design task is posed as a bicriteria nonlinear programming problem that considers the simultaneous optimization of the plant profitability and the energy consumption. The capabilities of the proposed methodology are illustrated through a 120,000,000 kg/year corn‐based bioethanol plant considering weather data of Tarragona (Spain). © 2013 American Institute of Chemical Engineers AIChE J 60: 500–506, 2014     

Design‐based life cycle assessment of hazardous air pollutant control options at pulp and paper mills: a comparison of thermal oxidation to photocatalytic oxidation and biofiltration

BACKGROUND: The forest products industry produces valuable industrial chemicals, wood products, and consumer goods, but is also responsible for the emission of significant quantities of hazardous air pollutants. Although many air pollution control options are available, little is known about the overall environmental impacts of implementing each option. Therefore, a life cycle assessment (LCA) was conducted to compare energy and raw material inputs, air emissions, and environmental impacts associated with construction and operation of two air pollution control systems: regenerative thermal oxidation (RTO) with wet scrubbing and photocatalytic oxidation (PCO) with biofiltration. RESULTS: LCA results indicated that environmental impacts to resource depletion, photochemical oxidant formation, and acidification were 20% higher for the use of a RTO‐scrubber than for the PCO‐biofilter. In addition, at least 25% of the RTO impacts were due to infrastructure requirements. However, the PCO‐biofilter system was responsible for more environmental impact in categories of global warming and human toxicity, because of the packing materials required and the electricity use for the PCO reactor. CONCLUSIONS: The PCO‐biofilter system could be a promising, environmentally‐friendly alternative to traditional RTO devices, provided that this system is modified to decrease resource and energy demands. Copyright © 2008 Society of Chemical Industry     

Multiobjective optimization of multiproduct batch plants scheduling under environmental and economic concerns

In batch process scheduling, production trade‐offs arise from the simultaneous consideration of different objectives. Economic goals are expressed in terms of plant profitability and productivity, whereas the environmental objectives are evaluated by means of metrics originated from the use of life cycle assessment methodology. This work illustrates a novel approach for decision making by using multiobjective optimization. In addition, different metrics are proposed to select a possible compromise based on the distance to a nonexistent utopian solution, whose objective function values are all optimal. Thus, this work provides a deeper insight into the influence of the metrics selection for both environmental and economic issues while considering the trade‐offs of adopting a particular schedule. The use of this approach is illustrated through its application to a case study related to a multiproduct acrylic fiber production plant, special attention is put to the influence of product changeovers. © 2010 American Institute of Chemical Engineers AIChE J, 57: 2766–2782, 2010     

Flexibility assessment and risk management in supply chains

Increased uncertainty in recent years has led the supply chains to incorporate measures to be more flexible in order to perform well in the face of the uncertain events. It has been shown that these measures improve the performance of supply chains by mitigating the risks associated with uncertainties. However, it is also important to assess the uncertainty under which a supply chain network can perform well and manage risk. Flexibility is defined in terms of the bounds of uncertain parameters within which supply chain operation is feasible. A hybrid simulation‐based optimization framework that uses two‐stage stochastic programming in a rolling horizon framework is proposed. The framework enables taking optimum planning decisions considering demand uncertainty while managing risk. The framework is used to study the trade‐offs between flexibility, economic performance, and risk associated with supply chain operation. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4166–4178, 2015     

Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

This investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.     

Nichtlineare stochastische Optimierung unter Unsicherheiten

Nonlinear Stochastic Optimization under Uncertainty Robust decision making under uncertainty is considered to be of fundamental importance in numerous disciplines and application areas. In dynamic chemical processes in particular there are parameters which are usually uncertain, but may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of the stochastic property of the uncertainties in the optimization approach is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. A new approach is presented and applied for stochastic optimization problems of batch distillation with a detailed dynamic process model.     

Comparison of Oleo‐ vs Petro‐Sourcing of Fatty Alcohols via Cradle‐to‐Gate Life Cycle Assessment

Alcohol ethoxylates surfactants are produced via ethoxylation of fatty alcohol (FA) with ethylene oxide. The source of FA could be either palm kernel oil (PKO) or petrochemicals. The study aimed to compare the potential environmental impacts for PKO‐derived FA (PKO‐FA) and petrochemicals‐derived FA (petro‐FA). Cradle‐to‐gate life cycle assessment has been performed for this purpose because it enables understanding of the impacts across the life cycle and impact categories. The results show that petro‐FA has overall lower average greenhouse gas (GHG) emissions (～2.97 kg CO₂e) compared to PKO‐FA (～5.27 kg CO₂e). (1) The practices in land use change for palm plantations, (2) end‐of‐life treatment for palm oil mill wastewater effluent and (3) end‐of‐life treatment for empty fruit bunches are the three determining factors for the environmental impacts of PKO‐FA. For petro‐FA, n‐olefin production, ethylene production and thermal energy production are the main factors. We found the judicious decisions on land use change, effluent treatment and solid waste treatment are key to making PKO‐FA environmentally sustainable. The sensitivity results show the broad distribution for PKO‐FA due to varying practices in palm cultivation. PKO‐FA has higher impacts on average for 12 out of 18 impact categories evaluated. For the base case, when accounted for uncertainty and sensitivity analyses results, the study finds that marine eutrophication, agricultural land occupation, natural land occupation, fossil depletion, particulate matter formation, and water depletion are affected by the sourcing decision. The sourcing of FA involves trade‐offs and depends on the specific practices through the PKO life cycle from an environmental impact perspective.     

Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns

The simultaneous consideration of economic and environmental objectives in batch production scheduling is today a subject of major concern. However, it constitutes a complex problem whose solution necessarily entails production trade‐offs. Unfortunately, a rigorous multiobjective optimization approach to solve this kind of problem often implies high computational effort and time, which seriously undermine its applicability to day‐to‐day operation in industrial practice. Hence, this work presents a hybrid optimization strategy based on rigorous local search and genetic algorithm to efficiently deal with industrial scale batch scheduling problems. Thus, a deeper insight into the combined environmental and economic issues when considering the trade‐offs of adopting a particular schedule is provided. The proposed methodology is applied to a case study concerning a multiproduct acrylic fiber production plant, where product changeovers influence the problem results. The proposed strategy stands for a marked improvement in effectively incorporating multiobjective optimization in short‐term plant operation. © 2012 American Institute of Chemical Engineers AIChE J, 59: 429–444, 2013