Search

• A mathematical model is proposed for tissue paper drying process. • The model realizes the accurate simulation of key parameters in the drying process. • The model provides global optimization scheme for key operating parameters. • The model improves the competitiveness by reducing 8.72% of the drying cost. The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%. [ABSTRACT FROM AUTHOR]

• A new generic approach in global optimization using cutting plane decomposition. • Use of AI in outer approximation and equality relaxation problems. • Data-enabled lower space decomposition methods in non-convex optimization formulations. • Development of a new metric (affinity) to assess and screen cutting planes. • Significant improvements (40-80%) in closing duality gaps in non-convex quadratic problems. Data-driven technologies have demonstrated their potential on various scientific and industrial applications. Their use in the development of generic optimization algorithms is relatively unexplored. The paper presents such an application to design a global optimization algorithm that is generic and suitable to address quadratic box constraint problems. The new method reformulates cutting plane decomposition methods substituting the solution of the master problem by a data-driven selection of cutting planes. The paper presents the theoretical background, data technologies used and computational results that compare the new against state-of-the-art methods. Computational experiments include 100 quadratic programming (QP) problems featuring a wide range of density (25-75%), size (40-100 variables), and complexity. Results are particularly encouraging and demonstrate significant reductions in the duality gap, as high as 40-60% scope on average. Largest improvements are traced in larger formulations (over 100 variables, 75% density). The research is based solely on data produced at a particular iteration. Future work is intended to extend the analysis comparing and considering data patterns across different iterations, also to apply the methodology in other classes of optimization problems. [ABSTRACT FROM AUTHOR]

This paper addresses the design and planning of manufacturing networks considering the option of centralized and distributed facilities, taking into account the potential trade-offs between investments and transportation. The problem is formulated as an extension of the Capacitated Multi-facility Weber Problem, which involves the selection of which facilities to build in each time period, and their location in the continuous two-dimensional space, in order to meet demand and minimize costs. The model is a multi-period GDP, reformulated as a nonconvex MINLP. We propose an accelerated version of the Bilevel Decomposition by Lara et al. (2018) that finds stronger bounds in the decomposition scheme. We benchmark the performance of our algorithm against the original Bilevel Decomposition and commercial global solvers and show that our approach outperforms the others in all instances tested. Additionally, we illustrate the applicability of the proposed model and solution framework with a biomass supply chain case study. [ABSTRACT FROM AUTHOR]

Highlights • A self-adaptive differential evolution optimization method is proposed. • The proposed method enhances the convergence speed and the exploitation capabilities. • The performance of the proposed method outperforms the original differential evolution method. • The operational parameters in ES-SAGD process are optimized to achieve maximal NPV. Abstract Differential evolution (DE) algorithm has shown good performance in many optimization problems. However, its control parameters greatly affect its performance and require many trials to determine the optimum values of control parameters for each specific optimization problem. In this paper, we present a self-adaptive DE with a new adaptation technique to improve the solution quality as well as increase the speed of convergence with a reduction in the computational cost. The proposed approach, called modified self-adaptive differential evolution (MSaDE), employs a new success-rate indicator of the strategies used to generate the trial vectors in conventional self-adaptive differential evolution (SaDE) algorithm. The proposed method has been tested on 22 benchmark problems and on the expanded solvent steam-assisted gravity drainage (ES-SAGD) recovery process. The results show that a significant speed-up is achieved in the exploitation and exploration capabilities of the self-adaptive DE algorithm. [ABSTRACT FROM AUTHOR]

• This paper presents the optimal design of distillation columns using Set Trimming and Enumeration. • The Set Trimming algorithm reduces the number of design solution candidates to evaluate. • The smart enumeration algorithm guarantees global optimality and has a lower computational effort than complete enumeration. • The scheme Set Trimming followed by Enumeration is robust and does not fail. In this article, we present an alternative approach to the use of metaheuristic methods (GA, PSO, SA, etc.) or mathematical programming (MINLP solvers) for the optimal design of one-feed-two-products distillation columns. We propose the use of Set Trimming followed by candidate enumeration. For the evaluation of the performance of each candidate solution, the method relies on solving the associated system of equations. Three different enumeration procedures are tested: Exhaustive Enumeration, Smart Enumeration, and Segmental Smart Enumeration. Smart Enumeration is an optimization procedure that identifies the solution through a search in the set of candidates organized in ascending order of the objective function lower bound, while Segmental Smart Enumeration is introduced in this article. We compare the results of the proposed procedure with the results using an MINLP approach with different solvers. Numerical results indicate that the best alternative of the enumeration algorithms can identify the global optimum faster than a global solver of mathematical optimization. Numerical tests also showed local solvers which attained optimal solutions quickly but may be trapped in a local optimum. [ABSTRACT FROM AUTHOR]

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review Generalized Disjunctive Programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges. [ABSTRACT FROM AUTHOR]

The paper focuses on the refinery-wide planning operations under uncertainties in product demand and price via robust optimization framework coupled with global optimization. Industrial/simulation data sets are used to build explicit nonlinear surrogate models for correlating the yield/property of products and the operational conditions of secondary processing units. A large-scale nonconvex mixed integer nonlinear programming (MINLP) model is formulated. The product demand and sale price uncertainties are incorporated into the proposed model via the "interval" and "interval + ellipsoidal" uncertainty set, respectively, to obtain the robust counterpart optimization model. A global optimization framework which combines the enhanced normalized multiparametric disaggregation technique (ENMDT) and the optimality-based bound tightening is developed. The proposed model and the global optimization algorithm are applied to an industrial refinery site with three illustrative cases. The results show the value of formulating nonlinear models for secondary processing units and the benefits from applying ENMDT-based algorithm. [ABSTRACT FROM AUTHOR]

• Comprehensive consideration of all kinds of uncertainties in the refinery hydrogen systems to simulate the actual operation of hydrogen networks. • A holistic modeling framework which integrates stochastic programming with flexibility constraints to show different conservativeness toward multi-scale uncertainties. • A good trade-off between the economic performance and the robustness of operation. • A tailored global optimization algorithm based on multiparametric disaggregation technique that reduces the optimality gap by orders of magnitude for large-scale process network problems with numerous bilinear terms. • The quantification of the value of stochastic solution (VSS) as the key measure for assessing the benefits of the stochastic programming. Robust design of hydrogen systems for large-scale refineries under multi-scale uncertainties has great potential for energy saving and cost reduction. The operation of hydrogen network is greatly affected by the rapidly changing market, deteriorating crude feedstock and varying operating conditions. In this paper, we propose a two-stage stochastic programming with flexibility constraints modeling framework that accounts for multi-scale uncertainties in an integrated strategy, aiming to achieve a good trade-off between the economic performance and the robustness of operation. A tailored-made global optimization algorithm based on multiparametric disaggregation technique is developed to tackle the computational challenge of the resulting MINLP problem with numerous bilinear terms. To illustrate the applicability of the proposed modeling framework and solution algorithm, studies on the optimal design of an industrial hydrogen system are presented. Multi-scenario operation strategy increases the operational flexibility and greatly reduces the total annualized cost in terms of the value of stochastic solution. [ABSTRACT FROM AUTHOR]

• A disjunctive optimization model for shell and tube heat exchanger is developed. • The model includes automatically selection of heat transfer enhancement techniques. • Detailed geometrical components of exchanger can be obtained after optimization. • Optimal solutions include the investigation of local and global optimums. • Performance and benefits of proposed model are demonstrated. Heat transfer enhancement (HTE) is an efficient technology to improve the performance of shell and tube heat exchangers (STHEs). While there exist many commercial software packages, heat exchanger design still highly depends on users' experiences, resulting in long computing time and inferior solutions. Therefore, a systematic optimization methodology is required in order to achieve an efficient and accurate design solution. This paper presents a generalized disjunctive programming (GDP) model for the optimization of STHE design. It is formulated as a mixed integer non-linear programming (MINLP) problem, involving selection for 12 technology combinations (four tube-side techniques, three shell-side techniques), and all discrete decisions for each selection are modeled by disjunctions. The model is then applied to minimize the total capital cost. Both global optimization solver BARON, and general MINLP solver DICOPT are tested. The results show that the developed method provides a better design solution compared with conventional STHE design procedures. [ABSTRACT FROM AUTHOR]