Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty

In this paper, we address the strategic planning of integrated bioethanol–sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.     

Multisite facility network integration design and coordination: An application to the refining industry

This paper addresses the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries using different crude combination alternatives. In addition, production capacity expansion requirements are also accounted for. The main feature of the paper is the development of a methodology for simultaneous analysis of process network integration alternatives in a multisite refining system through a mixed-integer linear program (MILP) with the overall objective of minimizing total annualized cost. The State Equipment Network (SEN) representation was used for modeling the network as it provides a consistent modeling strategy and proper handling of units that operate under different operating modes, which is common in the refining industry. The integrated network design specifically addresses intermediate material transfer between processing units at each site. The performance of the proposed model was tested on several industrial-scale examples to illustrate the economic potential and trade-offs involved in the optimization of the network. The use of mathematical programming models on an enterprise-wide scale to address strategic decisions considering various process integration alternatives yielded substantial benefits. These benefits not only materialize in terms of economic considerations, but also in terms of process flexibility and improvements in the understanding of the process interactions and systems limitations. Although the methodology was applied on a network of refineries, it can be readily extended to cover any network of continuous chemical processes.     

Graph theory augmented math programming approach to identify minimal reaction sets in metabolic networks

Bioprocesses are of growing importance as an avenue to produce chemicals. Microorganisms containing only desired catalytic and replication capabilities in their metabolic pathways are expected to offer efficient processes for chemical production. Realizing such minimal cells is the holy grail of metabolic engineering. In this paper, we propose a new method that combines graph-theoretic approaches with mixed-integer liner programming (MILP) to design metabolic networks with minimal reactions. Existing MILP based computational approaches are computationally complex especially for large networks. The proposed graph-theoretic approach offers an efficient divide-and-conquer strategy using the MILP formulation on sub-networks rather than considering the whole network monolithically. In addition to the resulting improvement in computational complexity, the proposed method also aids in identifying the key reactions to be knocked-out in order to achieve the minimal cell. The efficacy of the proposed approach is demonstrated using three case studies from two organisms, Escherichia coli and Saccharomyces cerevisiae.     

Resilience-aware design of interconnected supply chain networks with application to water-energy nexus

The effects of natural disasters, pandemic-induced lockdowns, and other disruptions often cascade across networks. In this work, we use minimum cost of resilience (MCOR) and operation-based resilience metrics to quantify network performance against single-connectivity failures and identify critical connections in interconnected networks. MCOR corresponds to the minimum additional infrastructure investment that is required to achieve a certain level of resilience. To guarantee MCOR, we incorporate the metrics in a multi-scenario mixed-integer linear program (MILP) that accounts for resilience in the design phase of interconnected networks. The goal is to obtain optimal generation and transportation capacities with flexible operation under all single-connectivity disruption scenarios. We demonstrate the applicability of our resilience-aware framework on a water-energy nexus (WEN) example focusing on grass-root design and retrofitting. We further apply the framework to analyze a regional WEN and observe that it is possible to achieve “full” resilience in the expense of additional regional investments.     

Multi-period design and planning model of shale gas field development

Long-term design and planning of shale gas field development is challenging due to the complex development operations and a wide range of candidate locations. In this work, we focus on the multi-period shale gas field development problem, where the shale gas field has multiple formations and each well can be developed from one of several alternative pads. The decisions in this problem involve the design of the shale gas network and the planning of development operations. A mixed-integer linear programming (MILP) model is proposed to address this problem. Since the proposed model is a large-scale MILP, we propose a solution pool-based bilevel decomposition algorithm to solve it. Results on realistic instances demonstrate the value of the proposed model and the effectiveness of the proposed algorithm.     

Optimal planning and scheduling of batch plants with operational uncertainties: An industrial application to Baker's yeast production

In this work, the mixed integer linear programming (MILP) model developed in Orçun et. al 1996 for optimal planning and scheduling of batch process plants under uncertain operating conditions is further improved to deal also with discrete probability functions. Furthermore, the logic behind integrating the processing uncertainties within the MILP model is implemented on the variations in the production volumes that can be faced in some batch processes such as Baker's yeast production. The modified model is tested on Baker's yeast production plant data to illustrate the effect of uncertainties on the production planning and scheduling. The results show that the plant production will be improved by 20% when the optimal production planning and scheduling is utilized by fine tuning the degree of risk the management can resist. An example on how a process design engineer may utilize such an MILP model for optimal planning and scheduling of batch process plant and identify plant problems, such as the bottleneck operations, is also included. A simulation type analysis on how to improve the processing site, i.e. the effect of introducing an extra operator to the bottleneck operation, is also demonstrated in this work using the available plant data.     

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     

Hybrid simulation based optimization approach for supply chain management

In this work, we propose a hybrid simulation optimization approach that addresses the problem of supply chain management. We formulated the problem as a mathematical model which minimizes the summation of production cost, transportation cost, inventory holding and shortage costs, subject to capacity and inventory balance constraints and propose a hybrid approach combining mathematical programming and simulation model to solve this problem. The main objective of this approach is to overcome the computational complexity associated with solving the underlying large-scale mixed integer linear problem and to provide a better representation of supply chain reality. The simulation-based optimization strategy uses an agent-based system to model the supply chain network. Each entity in the supply chain is represented as an agent whose activity is described by a collection of behavioral rules. The overall system is coupled with an optimization algorithm that is designed to address planning and scheduling level decisions.     

Cascading failure invulnerability analysis of chemical material network considering failure propagation capability

The material network in the chemical process has the characteristics of a complex network. Once the fault occurs, it will spread and lead to cascading failures, causing the chemical production process to not proceed normally. Previous studies of cascading failures have been limited by the size and characteristics of the network, which prevented the definition of the fault propagation capacity of the edge based on the actual situation. Studies on the effects of network topology and certain risk factors on the amount of fault propagation still need to be completed. Because of the above shortcomings, this study constructs a chemical material network cascading failure invulnerability analysis model that considers the failure propagation ability. Through the study of the chemical material network topology structure and the analysis of various flammable and explosive chemicals, the paper defines the fault propagation coefficient, which affects the load propagation quantity on the connecting edge. Then, based on the load-capacity nonlinear cascading failure model, the chemical material network's invulnerability was analyzed by adjusting the model parameters. The effectiveness of the model is verified through case analysis. Compared to the existing model, the model in this paper effectively improves the invulnerability of the material network and can reduce the phenomenon of large-scale failure of the material network due to the failure propagation capability is not considered.     

线性系统非冗余测量网络的优化设计

研究了线性系统非冗余测量网络设计的问题 ,采用混合整数线性规划方法与图论方法相结合建立了考虑可靠性以及精度的要求下建设费用最小的模型 .此模型适用于采用多块测量仪表测量同一物流的问题 ,求解此模型可以确保得到全局最优解 .对文献中例题的计算结果说明了本文方法的正确性     

石化企业蒸汽动力系统优化设计建模及应用

在石化企业蒸汽动力系统(SPS)热力学分析与数学规划集成优化策略的指导下,建立了多周期设计与运行协同优化的混合整数线性规划模型(MILP)。通过热力学效率、电热供应比2个指标筛选组合子系统,采用设计负荷离散化的方法确定SPS初始超结构,大大降低了优化设计问题模型的规模和复杂度。案例分析部分采用提出的优化设计方法和建模求解策略,得到了最优设计和运行方案,同传统设计方法相比,模型求解时间大大降低,方案可操作性大大提高,证实了文中提出的优化设计策略、筛选规则和优化设计模型的可行性和有效性。     

Multi-period synthesis of optimally integrated biomass and bioenergy supply network

This contribution addresses the multi-period synthesis of an optimally integrated regional biomass and bioenergy supply network through a mixed-integer linear programing (MILP) approach. The production processes from different sources of biomass include first, second, and third generations of biofuels like bioethanol, biodiesel, hydrogen, Fischer Tropsch diesel, and green gasoline. The aim is to maximize the sustainably viable utilization of resources by accounting for the competition between fuels and food production. An MILP model for efficient bioenergy network optimization based on four layers is extended to include several features, such as seasonality and availability of resources, enabling recycles of products and total site heat integration in order to address real-world applications with a systematic decision-making approach. The multi-period optimization of a heat-integrated biorefinery's supply network is performed through maximization of the economic performance. Economically efficient solutions are obtained with optimal selection of raw materials, technologies, intermediate and final product flows, and reduced greenhouse-gas emissions.     

不确定条件下炼化企业计划与调度整合策略

A strategy for the integration of production planning and scheduling in refineries is proposed.This strategy relies on rolling horizon strategy and a two-level decomposition strategy.This strategy involves an upper level multiperiod mixed integer linear programming(MILP) model and a lower level simulation system,which is extended from our previous framework for short-term scheduling problems [Luo,C.P.,Rong,G.,"Hierarchical approach for short-term scheduling in refineries",Ind.Eng.Chem.Res.,46,3656-3668(2007)].The main purpose of this extended framework is to reduce the number of variables and the size of the optimization model and,to quickly find the optimal solution for the integrated planning/scheduling problem in refineries.Uncertainties are also considered in this article.An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.     

Gedanken zur Zukunft der Feststoffverfahrenstechnik

On the Future of Solid Processing Techniques The chemical industry is experiencing major changes. The experts of the “Solids Processing Industrial Network” expect that in the future two kinds of chemical plants will develop: on the one hand the polyvalent, multipurpose, modular plant and on the other hand the world‐scale plants. At the same time the high percentage of solids as products of chemical processes will continue to increase and therefore new challenges have to be met. Small‐scale production of solids has to be customized to the individual customer needs, which means that product design will be a technology that defines the competitiveness. For large plants techniques have to be developed that enable large volumes to be produced but that also allow the reliable scale‐up from laboratory scale to world‐scale production. Automatization will play a crucial role in the success of solids processing, which will depend on the availability of on‐line measurement systems and the development of simulation models for the processes and the particle properties involved.     

Multistage stochastic programming for the closed-loop supply chain planning with mobile modules under uncertainty

Traditional supply chains usually follow fixed facility designs which coincide with the strategic nature of supply chain management (SCM). However, as the global market turns more volatile, the concept of mobile modularization has been adopted by increasingly more industrial practitioners. In mobile modular networks, modular units can be installed or removed at a particular site to expand or reduce the capacity of a facility, or relocated to other sites to tackle market volatility. In this work, we formulate a mixed-integer linear programming (MILP) model for the closed-loop supply chain network planning with modular distribution and collection facilities. To further deal with uncertain customer demands and recovery rates, we extend our model to a multistage stochastic programming model and efficiently solve it using a tailored stochastic dynamic dual integer programming (SDDiP) with Magnanti-Wong enhanced cuts. Computational experiments show that the added Magnanti-Wong cuts in the proposed algorithm can effectively close the gap between upper and lower bounds, and the benefit of mobile modules is evident when the temporal and spatial variability of customer demand is high.     

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement, a novel three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants is proposed and evaluated. A decoupling strategy is proposed for the solution of the three-scale model, which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling. This optimization framework simplifies the fundamental mixed-integer nonlinear programming (MINLP) into several sub-models, and improves the interpretability and extendibility. In the evaluation of an industrial case, a profit increase at a percentage of 3.25% is attained in optimization compared to the practical operations. Further sensitivity analysis is carried out for strategy evolving study when price policy, supply chain, and production requirement parameters are varied. These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.     

Decision support for integrated refinery supply chains: Part 2. Design and operation

Supply chain management has continually attracted much attention as companies are constantly looking into areas where they can cut costs and improve profit margin while maintaining customer satisfaction. Optimizing design and operation of the supply chain is vital for this purpose. Simulation models that capture the dynamics and uncertainties of the supply chain can be used to effectively conduct design and operation optimization studies. In Part 1 of this two-part paper, we proposed an integrated refinery supply chain dynamic simulator called Integrated Refinery In-Silico (IRIS). Here, we demonstrate the application of IRIS to provide decision support for optimal refinery supply chain design and operation based on a simulation–optimization framework. Three case studies are presented: identifying the optimal strategy to deal with supply disruptions, optimization of design decisions regarding additional capacity investments, and optimization of policies’ parameters. These decisions are optimized for two objectives: profit margin and customer satisfaction. The framework consists of a linkage between IRIS and a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. Results indicate that the proposed framework works well for supporting policy and investment decisions in the integrated refinery supply chain.     

Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling

System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from large-scale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure. After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.     

Long-term turnaround planning for integrated chemical sites

An integrated chemical site involves a complex network of chemical plants. Typically, these plants interact closely, are dependent on each other for raw materials and demand for their products, and have the provision of intermediate storage tanks to help manage inventory at strategic points in the network. Disruptions in the operation of these plants can drastically affect flow of material in the site network. As a result, the choice of sequence and timing of planned periodic turnarounds, which are major disruptions, is important in order to minimize effects on profits and production. We investigate a discrete-time mixed-integer linear programming (MILP) model to perform turnaround optimization. The objective is to recommend potential schedules in order to minimize losses while satisfying network, resource, turnaround, demand, financial and other practical constraints. We propose general formulations to tackle this problem and study an industrial-size site network under various scenarios over a long-term horizon.     

Supply chain redesign and new process introduction in multipurpose plants

Supply chain redesign involves decisions on facility location, relocation, investment, disinvestment, technology upgrade, production-allocation, distribution, supply contracts, capital generation, etc. In a previous publication (Naraharisetti et al., 2008), a mixed integer linear programming (MILP) model was developed for plants that use dedicated production processes. However, many batch plants such as those producing pharmaceuticals, paints, agricultural products, fine polymers, electronics, etc. are multi-purpose and can switch their products over time. Continuous plants such as refineries may also operate in different production modes. In this work, the process (a process refers to a stoichiometry through which raw materials are processed to give the products) used in a given production plant is allowed to vary from period to period and also within a period, for example, a production plant may produce both paint A and paint B in the same period and paint C in the next. An MILP model is developed for supply chain redesign in such plants. Using the MILP model and an illustrative example, we demonstrate new process introductions.