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     

Operational strategy and planning for raw natural gas refining complexes: Process modeling and global optimization

Optimal operational strategy and planning of a raw natural gas refining complex (RNGRC) is very challenging since it involves highly nonlinear processes, complex thermodynamics, blending, and utility systems. In this article, we first propose a superstructure integrating a utility system for the RNGRC, involving multiple gas feedstocks, and different product specifications. Then, we develop a large‐scale nonconvex mixed‐integer nonlinear programming (MINLP) optimization model. The model incorporates rigorous process models for input and output relations based on fundamentals of thermodynamics and unit operations and accurate models for utility systems. To reduce the noncovex items in the proposed MINLP model, equivalent reformulation techniques are introduced. Finally, the reformulated nonconvex MINLP model is solved to global optimality using state of the art deterministic global optimization approaches. The computational results demonstrate that a significant profit increase is achieved using the proposed approach compared to that from the real operation. © 2016 American Institute of Chemical Engineers AIChE J, 63: 652–668, 2017     

A new optimization model and a customized solution method for natural gas production network design and operation

This article proposes to tackle integrated design and operation of natural gas production networks under uncertainty, using a new two‐stage stochastic programming model, a novel reformulation strategy, and a customized global optimization method. The new model addresses material balances for multiple key gas components, pressure flow relationships in gas wells and pipelines, and compressor performance. This model is a large‐scale nonconvex mixed‐integer nonlinear programming problem that cannot be practically solved by existing global optimization solvers or decomposition‐based optimization methods. With the new reformulation strategy, the reformulated model has a better decomposable structure, and then a new decomposition‐based global optimization method is developed for efficient global optimization. In the case study of an industrial naturals production system, it is shown that the proposed modeling and optimization methods enable efficient solution, and the proposed optimization method is faster than a state‐of‐the‐art decomposition method by at least an order of magnitude. © 2016 American Institute of Chemical Engineers AIChE J, 63: 933–948, 2017     

An adaptive parallel tempering method for the dynamic data‐driven parameter estimation of nonlinear models

An adaptive parallel tempering algorithm is developed in a user‐friendly fashion that efficiently and robustly generates near‐optimum solutions. Using adaptive, implicit, time‐integration methods, the method allows fitting model parameters to dynamic data. The proposed approach is relatively insensitive to the initial guess and requires minimal fine‐tuning: most of the algorithm parameters can be determined adaptively based on the analysis of few model simulations, while default values are proposed for the few remaining ones, the exact values of which do not sensitively affect the solution. The method is extensively validated through its application to a number of algebraic and dynamic global optimization problems from Chemical Engineering literature. We then apply it to a multi‐parameter, highly nonlinear, model of the rheology of a thixotropic system where we show how the present approach can be used to robustly determine model parameters by fitting to dynamic, large amplitude, oscillatory stress vs. shear rate, data. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1937–1958, 2017     

Tactical capacity planning for semiconductor manufacturing: MILP models and scalable distributed parallel algorithms

A multiperiod stochastic mixed‐integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in‐process inventory, demand and capacity variability, multisite production, capacity utilization rate, and downside risk management. Both planning level decisions (i.e., capacity allocation and customer service level decisions) as well as operational level decisions (i.e., production, inventory, and shipment decisions) can be simultaneously determined based on the two proposed multiobjective optimization models. To address the huge number of scenarios needed to characterize the uncertainty and the large number of first‐stage integer variables in industrial scale applications, two novel scalable distributed parallel optimization algorithms are developed to mitigate the computational burden. The proposed mathematical models and algorithms are illustrated through two case studies from a major US semiconductor manufacturer. Results from these case studies provide key decision support for capacity expansion in semiconductor industry. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3930–3946, 2016     

General optimization model for the energy planning of industries including renewable energy: A case study on oil sands

A multi‐period optimization model is developed for the energy procurement planning of industries including renewable energy. The model is developed with the objective of identifying the optimal set of energy supply technologies to satisfy a set of demands (e.g., power, heat, hydrogen, etc.) and emission targets at minimum cost. Time dependent parameters are incorporated in the model formulation, including demands, fuel prices, emission targets, carbon tax, lead time, etc. The model is applied to a case study based on the oil sands operations over the planning period 2015–2050. Various production alternatives were incorporated, including renewable, nuclear, conventional and gasification of alternative fuels. The results obtained indicated that the energy optimization model is a practical tool that can be utilized for identifying the key parameters that affect the operations of energy‐intensive industrial operations, and can further assist in the planning and scheduling of the energy for these industries. © 2016 American Institute of Chemical Engineers AIChE J, 63: 610–638, 2017     

Energy optimization of water supply system scheduling: Novel MINLP model and efficient global optimization algorithm

This article is concerned with global optimization of water supply system scheduling with pump operations to minimize total energy cost. The scheduling problem is first formulated as a non‐convex mixed‐integer nonlinear programming (MINLP) problem, accounting for flow rates in pipes, operation profiles of pumps, water levels of tanks, and customer demand. Binary variables denote on–off switch operations for pumps and flow directions in pipes, and nonlinear terms originate from characteristic functions for pumps and hydraulic functions for pipes. The proposed MINLP model is verified with EPANET, which is a leading software package for water distribution system modeling. We further develop a novel global optimization algorithm for solving the non‐convex MINLP problem. To demonstrate the applicability of the proposed model and the efficiency of the tailored global optimization algorithm, we present results of two case studies with up to 4 tanks, 5 pumps, 5 check valves, and 21 pipes. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4277–4296, 2016     

A trust region filter method for glass box/black box optimization

Modern nonlinear programming solvers can be utilized to solve very large scale problems in chemical engineering. However, these methods require fully open models with accurate derivatives. In this article, we address the hybrid glass box/black box optimization problem, in which part of a system is modeled with open, equation based models and part is black box. When equation based reduced models are used in place of the black box, NLP solvers may be applied directly but an accurate solution is not guaranteed. In this work, a trust region filter algorithm for glass box/black box optimization is presented. By combining concepts from trust region filter methods and derivative free optimization, the method guarantees convergence to first‐order critical points of the original glass box/black box problem. The algorithm is demonstrated on three comprehensive examples in chemical process optimization. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3124–3136, 2016     

Optimization models for planning shale gas well refracture treatments

Refracturing is a promising option for addressing the characteristically steep decline curves of shale gas wells. In this work we propose two optimization models to address the refracturing planning problem. First, we present a continuous‐time nonlinear programming model based on a novel forecast function that predicts pre‐ and post‐treatment productivity declines. Next, we propose a discrete‐time, multi‐period mixed‐integer linear programming (MILP) model that explicitly accounts for the possibility of multiple refracture treatments over the lifespan of a well. In an attempt to reduce solution times to a minimum, we compare three alternative formulations against each other (big‐M formulation, disjunctive formulation using Standard and Compact Hull‐Reformulations) and find that the disjunctive models yield the best computational performance. Finally, we apply the proposed MILP model to two case studies to demonstrate how refracturing can increase the expected recovery of a well and improve its profitability by several hundred thousand USD. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4297–4307, 2016     

Global optimization for sustainable design and synthesis of algae processing network for CO2 mitigation and biofuel production using life cycle optimization

Global optimization for sustainable design and synthesis of a large‐scale algae processing network under economic and environmental criteria is addressed. An algae processing network superstructure including 7800 processing routes is proposed. Based on the superstructure, a multiobjective mixed‐integer nonlinear programming (MINLP) model is developed to simultaneously optimize the unit cost and the unit global warming potential (GWP). To efficiently solve the nonconvex MINLP model with separable concave terms and mixed‐integer fractional terms in the objective functions, a global optimization strategy that integrates a branch‐and‐refine algorithm based on successive piecewise linear approximations is proposed and an exact parametric algorithm based on Newton's method. Two Pareto‐optimal curves are obtained for biofuel production and biological carbon sequestration, respectively. The unit annual biofuel production cost ranges from $7.02/gasoline gallon equivalent (GGE) to $9.71/GGE, corresponding to unit GWP's of 26.491 to 16.52 kg CO₂‐eq/GGE, respectively. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3195–3210, 2014     

An optimization‐based approach for structural design of self‐assembled DNA tiles

DNA tiles are self‐assembled nanostructures, which offer exciting opportunities for synthesis of novel materials. A challenge for structural design of DNA tiles is to identify optimal locations for so‐called crossovers, which are bridges between DNA double helices formed by pairs of single‐stranded DNA. An optimization‐based approach is presented to identify optimal locations for such crossovers. Minimization of a potential‐energy model for a given structural design demonstrates the importance of local minima. Both deterministic global optimization of a reduced model and multistart optimization of the full model are applied successfully to identify the global minimum. MINLP optimization using a branch‐and‐bound algorithm (GAMS/SBB) identifies an optimal structural design of a DNA tile successfully with significant reduction in computational load compared to exhaustive enumeration, which demonstrates the potential of the proposed method to reduce trial‐and‐error efforts for structural design of DNA tiles. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1804–1817, 2017     

A planning model for multiple blending schemes☆

The key of production planning of refineries is to determine the production planning of units and blending schemes of blends in each period of the plan horizon, since they affect the effective utilization of components of refineries and hence profits. The optimization is difficult, because of many complicated product production–consumption relation-ships in production processes, which are closely related to the running modes of the units. Additional y, the blending products, such as gasoline and diesel, may use multiple blending schemes for their production that increase the complexity of the problem. This paper models the production planning problem as a mixed integer nonlinear programming. Computational experiments for a refinery show the effectiveness of the model. The optimal results give the effective utilization of the self-produced components and increase of the profit.     

A novel adaptive surrogate modeling‐based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations

A novel adaptive surrogate modeling‐based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed‐integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece‐wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191–4209, 2015     

Novel Natural Gas to Liquids Processes: Process Synthesis and Global Optimization Strategies

An optimization‐based process synthesis framework is proposed for the conversion of natural gas to liquid transportation fuels. Natural gas conversion technologies including steam reforming, autothermal reforming, partial oxidation to methanol, and oxidative coupling to olefins are compared to determine the most economic processing pathway. Hydrocarbons are produced from Fischer–Tropsch (FT) conversion of syngas, ZSM‐5 catalytic conversion of methanol, or direct natural gas conversion. Multiple FT units with different temperatures, catalyst types, and hydrocarbon effluent compositions are investigated. Gasoline, diesel, and kerosene are generated through upgrading units involving carbon‐number fractionation or ZSM‐5 catalytic conversion. A powerful deterministic global optimization method is introduced to solve the mixed‐integer nonlinear optimization model that includes simultaneous heat, power, and water integration. Twenty‐four case studies are analyzed to determine the effect of refinery capacity, liquid fuel composition, and natural gas conversion technology on the overall system cost, the process material/energy balances, and the life cycle greenhouse gas emissions. © 2013 American Institute of Chemical Engineers AIChE J, 59: 505–531, 2013     

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Multiscale models have been developed to simulate the behavior of spatially‐heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially‐dependent. While such a model provides an adequate representation of the catalytic reactor, model‐plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE‐based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2374–2390, 2016     

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     

Optimization of grade transitions in polyethylene solution polymerization processes

This study considers the development of optimization models for grade transition of polyethylene solution polymerization processes. A detailed mathematical model is developed to capture the dynamics of the solution polymerization process. This includes time delay models for vapor and liquid recycle streams as well as a reduced, yet accurate, vapor‐liquid equilibrium (VLE) model derived from rigorous VLE calculations. Simultaneous dynamic optimization approach is applied to solve the optimization problem to reduce off‐spec production time and transition time. Two optimization formulations, single stage and multistage, are developed to deal with single‐value target and specification bands of product properties, respectively. The results show significant reductions in grade transition time and off‐spec production time. In addition, the multistage formulation designed for problems with specification bands outperforms its single stage counterpart. It minimizes transition time and off‐spec production directly, and leads to higher performance control profiles. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1126–1142, 2016     

Global optimization of an industrial gas network operation

Air Liquide operates several industrial gas pipeline networks around the world, connecting air separation plants to customers of industrial gases. The operation of such a network of plants, pipelines, and customers is complicated due to fluctuating electricity prices and customer demands. We describe a complex industrial problem for real‐time optimization of network operations in the presence of these challenges. We then summarize a concerted modeling and algorithmic effort toward global optimization of this model. The resulting advances include development of a regression‐based fully‐deterministic nonconvex optimization model, a tool for diagnosing infeasibilities during model development, reformulations and scaling to make the model more amenable for optimization, and development of strengthened relaxations for its efficient solution. We provide details on the development of these tools and techniques that facilitated the solution of this model in a reasonable computational time with the global solver BARON. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3215–3224, 2016     

Superstructure optimization of integrated fast pyrolysis‐gasification for production of liquid fuels and propylene

Motivated by the apparent advantages of fast pyrolysis and gasification, a novel integrated biorefinery plant is systematically synthesized for coproducing premium quality liquid fuels and propylene. The required heat and fluidization promotion of the fast pyrolyzer are provided by hot syngas from the gasifier. Light gas and syngas from the fast pyrolyzer are finally converted to hydrocarbons via Fischer‐Tropsch synthesis. Multiple syngas production technologies, hydrocarbon production and downstream upgrading routes are incorporated within a superstructure optimization based process synthesis framework. This is the first article to investigate the benefits associated with the introduction of conventional catalytic cracking and dewaxing from a systems engineering perspective. Surrogate models describing the gasifiers and rigorous equations for Fischer‐Tropsch effluents validated by our experimental collaborator are introduced. Through investigation of five scenarios the primary parameters affecting overall economic performance are identified through ranking of the relevant candidates. Comparisons of the hybrid conversion route and stand‐alone routes are made. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3155–3176, 2016