Investment optimization model for freshwater acquisition and wastewater handling in shale gas production

Major challenges of water use in the drilling and fracturing process in shale gas production are large volumes required in a short‐period of time and the nonsteady nature of wastewater treatment. A new mixed‐integer linear programming (MILP) model for optimizing capital investment decisions for water use for shale gas production through a discrete‐time representation of the State‐Task Network is presented. The objective is to minimize the capital cost of impoundment, piping, and treatment facility, and operating cost including freshwater, pumping, and treatment. The goal is to determine the location and capacity of impoundment, the type of piping, treatment facility locations and removal capability, freshwater sources, as well as the frac schedule. In addition, the impact of several factors such as limiting truck hauling and increasing flowback volume on the solution is examined. A case study is optimized to illustrate the application of the proposed formulation. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1770–1782, 2015     

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     

Strategic planning,design, and development of the shale gas supply chain network

The long‐term planning of the shale gas supply chain is a relevant problem that has not been addressed before in the literature. This article presents a mixed‐integer nonlinear programming (MINLP) model to optimally determine the number of wells to drill at every location, the size of gas processing plants, the section and length of pipelines for gathering raw gas and delivering processed gas and by‐products, the power of gas compressors, and the amount of freshwater required from reservoirs for drilling and hydraulic fracturing so as to maximize the net present value of the project. Because the proposed model is a large‐scale nonconvex MINLP, we develop a decomposition approach based on successively refining a piecewise linear approximation of the objective function. Results on realistic instances show the importance of heavier hydrocarbons to the economics of the project, as well as the optimal usage of the infrastructure by properly planning the drilling strategy. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2122–2142, 2014     

Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016     

Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm

The optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR) is addressed. A two‐stage stochastic mixed‐integer linear fractional programming (SMILFP) model is developed to optimize the levelized cost of energy generated from shale gas. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end‐uses, and transportation. To reduce the model size and number of scenarios, we apply a sample average approximation method to generate scenarios based on the real‐world EUR data. In addition, a novel solution algorithm integrating the parametric approach and the L‐shaped method is proposed for solving the resulting SMILFP problem within a reasonable computational time. The proposed model and algorithm are illustrated through a case study based on the Marcellus shale play, and a deterministic model is considered for comparison. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3739–3755, 2015     

Game theory approach to optimal design of shale gas supply chains with consideration of economics and life cycle greenhouse gas emissions

This article addresses the optimal design of a non‐cooperative shale gas supply chain based on a game theory approach. Instead of assuming a single stakeholder as in centralized models, we consider different stakeholders, including the upstream shale gas producer and the midstream shale gas processor. Following the Stackelberg game, the shale gas producer is identified as the leader, whose objectives include maximizing its net present value (NPV) and minimizing the life cycle greenhouse gas (GHG) emissions. The shale gas processor is identified as the follower that takes actions after the leader to maximize its own NPV. The resulting problem is a multiobjective mixed‐integer bilevel linear programming problem, which cannot be solved directly using any off‐the‐shelf optimization solvers. Therefore, an efficient projection‐based reformulation and decomposition algorithm is further presented. Based on a case study of the Marcellus shale play, the non‐cooperative model not only captures the interactions between stakeholders but also provides more realistic solutions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2671–2693, 2017     

Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization

A novel process design for a more cost‐effective, greener process for making chemicals from shale gas and bioethanol is presented. The oxidative coupling of methane and cocracking technologies are considered for converting methane and light natural gas liquids, into value‐added chemicals. Overall, the process includes four process areas: gas treatment, gas to chemicals, methane‐to‐ethylene, and bioethanol‐to‐ethylene. A simulation‐optimization method based on the NSGA‐II algorithm for the life cycle optimization of the process modeled in the Aspen HYSYS is developed. An energy integration model is also fluidly nested using the mixed‐integer linear programming. The results show that for a “good choice” optimal design, the minimum ethylene selling price is $655.1/ton and the unit global‐warming potential of ethylene is 0.030 kg CO₂‐eq/kg in the low carbon shale gas scenario, and $877.2/ton and 0.360 kg CO₂‐eq/kg in the high carbon shale gas scenario. © 2014 American Institute of Chemical Engineers AIChE J, 61: 1209–1232, 2015     

Risk management for a global supply chain planning under uncertainty: Models and algorithms

In this article, we consider the risk management for mid‐term planning of a global multi‐product chemical supply chain under demand and freight rate uncertainty. A two‐stage stochastic linear programming approach is proposed within a multi‐period planning model that takes into account the production and inventory levels, transportation modes, times of shipments, and customer service levels. To investigate the potential improvement by using stochastic programming, we describe a simulation framework that relies on a rolling horizon approach. The studies suggest that at least 5% savings in the total real cost can be achieved compared with the deterministic case. In addition, an algorithm based on the multi‐cut L‐shaped method is proposed to effectively solve the resulting large scale industrial size problems. We also introduce risk management models by incorporating risk measures into the stochastic programming model, and multi‐objective optimization schemes are implemented to establish the tradeoffs between cost and risk. To demonstrate the effectiveness of the proposed stochastic models and decomposition algorithms, a case study of a realistic global chemical supply chain problem is presented. © 2009 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     

基于环境风险评价的页岩气水务管理框架及其模块分析

随着全球页岩气勘探开发的快速推进,页岩气开发的诸多环境问题也逐渐凸显,缺乏相应的环境评价和管理方法,尤其是在水务管理方面。提出了基于环境风险评价的页岩气水务管理框架;从钻井、水力压裂、采出水处理等3个阶段系统阐述和分析了页岩气水务管理中存在的风险源项,包括其污染通道和种类;结合环境风险评价方法的优缺点评估和应用前景,推荐采用故障树/事故树分析、概率风险评价法以及基于概率理论统计和模糊集的综合法进行页岩气水务管理风险评价。     

Multi‐period planning,design, and strategic models for long‐term,quality‐sensitive shale gas development

In this work we address the long‐term, quality‐sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large‐scale, nonconvex, mixed‐integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor‐designed solution strategy we identify near‐global solutions to the resulting large‐scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators’ profitability by several million U.S. dollars. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2296–2323, 2016     

Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain

The design and planning of an integrated ethanol and gasoline supply chain is addressed, and is composed of harvesting and production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain and different means of transportation, and model this multiscale design problem as a multiperiod MILP model. In order to identify regions where investments are needed and the optimal configuration of the network, a strategic planning model is considered in which gasoline stations are aggregated in different regions. A detailed formulation is considered where regions are disaggregated into gas stations to determine the retrofit projects for the selection of blending pumps over their expected life. Also, the application of these MILP models with two large‐scale problems are illustrated. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4655–4672, 2013     

Global superstructure optimization for the design of integrated process water networks

We propose a general superstructure and a model for the global optimization for integrated process water networks. The superstructure consists of multiple sources of water, water‐using processes, wastewater treatment, and pre‐treatment operations. Unique features are that all feasible interconnections are considered between them and multiple sources of water can be used. The proposed model is formulated as a nonlinear programing (NLP) and as a mixed integer nonlinear programing (MINLP) problem for the case when 0–1 variables are included for the cost of piping and to establish optimal trade‐offs between cost and network complexity. To effectively solve the NLP and MINLP models to global optimality we propose tight bounds on the variables, which are expressed as general equations. We also incorporate the cut proposed by Karuppiah and Grossmann to significantly improve the strength of the lower bound for the global optimum. The proposed model is tested on several examples. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Modular methanol manufacturing from shale gas: Techno‐economic and environmental analyses of conventional large‐scale production versus small‐scale distributed,modular processing

This article presents comparative techno‐economic and environmental analyses of four representative shale gas monetization options, namely, conventional shale gas processing, large‐scale methanol manufacturing, modular methanol manufacturing with shale gas supplied by pipelines, and modular methanol manufacturing with consideration of plant relocation. We first present shale gas supply models for the four gas monetization options. Next, the process designs for shale gas processing and methanol manufacturing from shale gas are described. We develop detailed process simulation models for shale gas processing and methanol manufacturing with different scales using raw shale gas extracted from the Marcellus, Eagle Ford, and Bakken shale plays. On this basis, techno‐economic analyses and environmental impact analyses are conducted for the four shale gas monetization options to systematically compare their economic and environmental performances based on the same conditions. The results show that modular methanol manufacturing is more economically competitive than conventional shale gas processing, although it leads to higher environmental impacts. Besides, modular methanol manufacturing is better than large‐scale methanol manufacturing for raw shale gas produced from distributed, remote wells from both economic and environmental perspectives. © 2017 American Institute of Chemical Engineers AIChE J, 64: 495–510, 2018     

A bi‐criterion optimization approach for the design and planning of hydrogen supply chains for vehicle use

In this article, we address the design of hydrogen supply chains for vehicle use with economic and environmental concerns. Given a set of available technologies to produce, store, and deliver hydrogen, the problem consists of determining the optimal design of the production‐distribution network capable of satisfying a predefined hydrogen demand. The design task is formulated as a bi‐criterion mixed‐integer linear programming (MILP) problem, which simultaneously accounts for the minimization of cost and environmental impact. The environmental impact is measured through the contribution to climate change made by the hydrogen network operation. The emissions considered in the analysis are those associated with the entire life cycle of the process, and are quantified according to the principles of Life Cycle Assessment (LCA). To expedite the search of the Pareto solutions of the problem, we introduce a bi‐level algorithm that exploits its specific structure. A case study that addresses the optimal design of the hydrogen infrastructure needed to fulfill the expected hydrogen demand in Great Britain is introduced to illustrate the capabilities of the proposed approach. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

A mechanistic model for the water‐gas shift reaction over noble metal substituted ceria

The water‐gas shift (WGS) reaction was carried out in the presence of Pd and Pt substituted nanocrystalline ceria catalysts synthesized by solution combustion technique. The catalysts were characterized by powder XRD and XPS. The noble metals were found to be present in ionic form substituted for the cerium atoms. The catalysts showed high activity for the WGS reaction with high conversions below 250°C. The products of reaction were only carbon dioxide and hydrogen, and no hydrocarbons were observed even in trace quantities. The reactions were carried out with different amounts of noble metal ion substitution and 2% Pt substituted ceria was found to be the best catalyst. The various possible mechanisms for the reaction were proposed and tested for their consistency with experimental data. The dual site mechanism best described the kinetics of the reaction and the corresponding rate parameters were obtained. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Optimization of large‐scale water transfer networks: Conic integer programming model and distributed parallel algorithms

The optimization of a multi‐echelon water transfer network (WTN) and the associate transportation and inventory systems with demand uncertainty is addressed in article. Optimal network structure, facility locations, operation capacities, as well as the inventory and transportation decisions can be simultaneously determined by the mixed integer nonlinear programming (MINLP) model which includes bilinear, square root and nonlinear fractional terms. By exploiting the properties of this model, we reformulate the MINLP problem as a conic integer optimization model. To overcome the memory and computing bandwidth limitations caused by the huge number of active nodes in the branch‐and‐bound search tree, novel distributed parallel optimization algorithms based on Lagrangean relaxation and message passing interface as well as their serial versions are proposed to solve the resulting conic integer programming model. A regional WTN in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithms. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1566–1581, 2017     

钢铁冶金企业给水排水工程设计节能途径

针对国家对钢铁行业节能减排及可持续发展的战略要求,作者从钢铁冶金企业给水排水工程设计出发;在满足工艺用水要求的前提下,对间接冷却循环水系统应优先采用软水密闭循环水系统;准确合理确定供水压力、供水流量;合理选择高效率水泵等几个方面的节能途径进行论述.提出了钢铁冶金企业给水排水工程设计节能途径.     

Multiechelon supply chain planning with sequence‐dependent changeovers and price elasticity of demand under uncertainty

An optimization framework is proposed for a multiechelon multiproduct process supply chain planning under demand uncertainty considering inventory deviation and price fluctuation. In this problem, the sequence‐dependent changeovers occur at the production plants, and price elasticity of demand is considered at the markets. Based on the classic formulation of travelling salesman problem (TSP), a mixed‐integer liner programming (MILP) is developed, whose objective function considers the profit, inventory deviations from the desired trajectories and price changes simultaneously. Model predictive control (MPC) approach is adopted to tackle the uncertain issues, as well as the inventory and price maintenance. The applicability of the proposed model and approach was illustrated by solving a supply chain example. Some issues, including length of the control horizon, price elasticity of demand, weights, inventory trajectories, and changeovers, are discussed based on the computational results. © 2012 American Institute of Chemical Engineers AIChE J, 2012