生物质热解制燃料油及化学品的工艺技术研究进展

从生物质热解制液体燃料油(生物油)的收率和品质两方面论述了生物质热解关键技术和热解制备液体燃料工艺。通过对比分析了传统的生物质快速热解关键技术———热解反应器、加料技术、气-固快速分离技术及热解蒸汽快速冷凝技术的研究现状、难点和不足,并提出了新型生物质快速热解关键技术———旋转筛板热解工艺。同时针对现行生物质热解制燃料油工艺存在的不足,对比分析了4种热解制取燃料油工艺,并提出了汽爆、固态发酵的生化转化与快速热解相结合制取液体燃料的方法。     

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     

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     

Global optimization of large‐scale mixed‐integer linear fractional programming problems: A reformulation‐linearization method and process scheduling applications

Mixed‐integer linear fractional program (MILFP) is a class of mixed‐integer nonlinear programs (MINLP) where the objective function is the ratio of two linear functions and all constraints are linear. Global optimization of large‐scale MILFPs can be computationally intractable due to the presence of discrete variables and the pseudoconvex/pseudoconcave objective function. We propose a novel and efficient reformulation–linearization method, which integrates Charnes–Cooper transformation and Glover's linearization scheme, to transform general MILFPs into their equivalent mixed‐integer linear programs (MILP), allowing MILFPs to be globally optimized effectively with MILP methods. Extensive computational studies are performed to demonstrate the efficiency of this method. To illustrate its applications, we consider two batch scheduling problems, which are modeled as MILFPs based on the continuous‐time formulations. Computational results show that the proposed approach requires significantly shorter CPU times than various general‐purpose MINLP methods and shows similar performance than the tailored parametric algorithm for solving large‐scale MILFP problems. Specifically, it performs with respect to the CPU time roughly a half of the parametric algorithm for the scheduling applications. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4255–4272, 2013     

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     

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     

Process synthesis using block superstructure with automated flowsheet generation and optimization

An alternative method for chemical process synthesis using a block‐based superstructure representation is proposed. The block‐based superstructure is a collection of blocks arranged in a two‐dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed‐integer nonlinear formulation for automated flowsheet generation and optimization‐based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas © 2018 American Institute of Chemical Engineers AIChE J, 64: 3082–3100, 2018     

An integrated framework for scheduling and control using fast model predictive control

Integration of scheduling and control involves extensive information exchange and simultaneous decision making in industrial practice (Engell and Harjunkoski, Comput Chem Eng. 2012;47:121–133; Baldea and Harjunkoski I, Comput Chem Eng. 2014;71:377–390). Modeling the integration of scheduling and dynamic optimization (DO) at control level using mathematical programming results in a Mixed Integer Dynamic Optimization which is computationally expensive (Flores‐Tlacuahuac and Grossmann, Ind Eng Chem Res. 2006;45(20):6698–6712). In this study, we propose a framework for the integration of scheduling and control to reduce the model complexity and computation time. We identify a piece‐wise affine model from the first principle model and integrate it with the scheduling level leading to a new integration. At the control level, we use fast Model Predictive Control (fast MPC) to track a dynamic reference. Fast MPC also overcomes the increasing dimensionality of multiparametric MPC in our previous study (Zhuge and Ierapetritou, AIChE J. 2014;60(9):3169–3183). Results of CSTR case studies prove that the proposed approach reduces the computing time by at least two orders of magnitude compared to the integrated solution using mp‐MPC. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3304–3319, 2015     

A projection‐based method for production planning of multiproduct facilities

An algorithm is presented for identifying the projection of a scheduling model's feasible region onto the space of production targets. The projected feasible region is expressed using one of two mixed‐integer programming formulations, which can be readily used to address integrated production planning and scheduling problems that were previously intractable. Production planning is solved in combination with a surrogate model representing the region of feasible production amounts to provide optimum production targets, while a detailed scheduling is solved in a rolling‐horizon manner to define feasible schedules for meeting these targets. The proposed framework provides solutions of higher quality and yields tighter bounds than previously proposed approaches. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

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     

Systematic modeling of discrete‐continuous optimization models through generalized disjunctive programming

Discrete‐continuous optimization problems are commonly modeled in algebraic form as mixed‐integer linear or nonlinear programming models. Since these models can be formulated in different ways, leading either to solvable or nonsolvable problems, there is a need for a systematic modeling framework that provides a fundamental understanding on the nature of these models. This work presents a modeling framework, generalized disjunctive programming (GDP), which represents problems in terms of Boolean and continuous variables, allowing the representation of constraints as algebraic equations, disjunctions and logic propositions. An overview is provided of major research results that have emerged in this area. Basic concepts are emphasized as well as the major classes of formulations that can be derived. These are illustrated with a number of examples in the area of process systems engineering. As will be shown, GDP provides a structured way for systematically deriving mixed‐integer optimization models that exhibit strong continuous relaxations, which often translates into shorter computational times. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3276–3295, 2013     

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     

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

With diversified requirements and varying manufacturing environments, the optimal production planning for a steelmill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming (MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome theweakness in solving the MINLP problem directly. The first one is to transformthe original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the originalmodel using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.     

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     

Integrated gasoline blending and order delivery operations: Part I. short‐term scheduling and global optimization for single and multi‐period operations

Gasoline is one of the most valuable products in an oil refinery and can account for as much as 60–70% of total profit. Optimal integrated scheduling of gasoline blending and order delivery operations can significantly increase profit by avoiding ship demurrage, improving customer satisfaction, minimizing quality give‐aways, reducing costly transitions and slop generation, exploiting low‐quality cuts, and reducing inventory costs. In this article, we first introduce a new unit‐specific event‐based continuous‐time formulation for the integrated treatment of recipes, blending, and scheduling of gasoline blending and order delivery operations. Many operational features are included such as nonidentical parallel blenders, constant blending rate, minimum blend length and amount, blender transition times, multipurpose product tanks, changeovers, and piecewise constant profiles for blend component qualities and feed rates. To address the nonconvexities arising from forcing constant blending rates during a run, we propose a hybrid global optimization approach incorporating a schedule adjustment procedure, iteratively via a mixed‐integer programming and nonlinear programming scheme, and a rigorous deterministic global optimization approach. The computational results demonstrate that our proposed formulation does improve the mixed‐integer linear programming relaxation of Li and Karimi, Ind. Eng. Chem. Res., 2011, 50, 9156–9174. All examples are solved to be 1%‐global optimality with modest computational effort. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2043–2070, 2016     

Continuous‐time modeling and global optimization approach for scheduling of crude oil operations

Scheduling of crude oil operations is a critical and complicated component of overall refinery operations, because crude oil costs account for about 80% of the refinery turnover. Moreover, blending with less expensive crudes can significantly increase profit margins. The mathematical modeling of blending different crudes in storage tanks results in many bilinear terms, which transforms the problem into a challenging, nonconvex, and mixed‐integer nonlinear programming (MINLP) optimization model. Two primary contributions have been made. First, the authors developed a novel unit‐specific event‐based continuous‐time MINLP formulation for this problem. Then they incorporated realistic operational features such as single buoy mooring (SBM), multiple jetties, multiparcel vessels, single‐parcel vessels, crude blending, brine settling, crude segregation, and multiple tanks feeding one crude distillation unit at one time and vice versa. In addition, 15 important volume‐based or weight‐based crude property indices are also considered. Second, they exploited recent advances in piecewise‐linear underestimation of bilinear terms within a branch‐and‐bound algorithm to globally optimize the MINLP problem. It is shown that the continuous‐time model results in substantially fewer bilinear terms. Several examples taken from the work of Li et al. are used to illustrate that (1) better solutions are obtained and (2) ε‐global optimality can be attained using the proposed branch‐and‐bound global optimization algorithm with piecewise‐linear underestimations of the bilinear terms. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

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     

Computer‐aided design of ionic liquids as solvents for extractive desulfurization

Although ionic liquids (ILs) have been widely explored as solvents for extractive desulfurization (EDS) of fuel oils, systematic studying of the optimal design of ILs for this process is still scarce. The UNIFAC‐IL model is extended first to describe the EDS system based on exhaustive experimental data. Then, based on the obtained UNIFAC‐IL model and group contribution models for predicting the melting point and viscosity of ILs, a mixed‐integer nonlinear programming (MINLP) problem is formulated for the purpose of computer‐aided ionic liquid design (CAILD). The MINLP problem is solved to optimize the liquid‐liquid extraction performance of ILs in a given multicomponent model EDS system, under consideration of constraints regarding the IL structure, thermodynamic and physical properties. The top five IL candidates preidentified from CAILD are further evaluated by means of process simulation using ASPEN Plus. Thereby, [C₅MPy][C(CN)₃] is identified as the most suitable solvent for EDS. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1013–1025, 2018     

On the high‐gasification rate of Brazilian manganese ore in chemical‐looping combustion (CLC) for solid fuels

The high rate of char gasification observed when using a Brazilian manganese ore as compared to ilmenite is investigated in a batch fluidized‐bed reactor. Experiments were carried out at 970°C using petroleum coke, coal and wood char as fuel with a 50% H₂O in N₂ as fluidizing gas. A manufactured manganese oxygen carrier was also used, however, which presented a slower char conversion rate than the manganese ore. It is concluded that decrease in H₂ inhibition and oxygen release are unlikely to be the main responsible mechanisms for the ore's unexpected gasification rate. The ore was also mixed in different ratios with ilmenite and it was observed that the presence of even small amounts of ore in the bed resulted in increased gasification rate. Thus, the high‐gasification rate for the manganese ore could be due to a contribution from the impurities in the ore by catalyzing the gasification reaction. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4346–4354, 2013