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• 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]

Recent developments in data science and machine learning have inspired a new wave of research into data-driven modeling for mathematical optimization of process applications. This paper first considers essential conditions for robustness to uncertainties and accurate extrapolation, which are required to integrate surrogates into process optimization. Next we consider two perspectives for developing process engineering surrogates: a surrogate-led and a mathematical programming-led approach. As these data-driven surrogate models must be integrated into a larger process optimization problem, we discuss the verification problem , i.e., checking that the optimum of the surrogate corresponds to the optimum of the truth model. The paper investigates two case studies on surrogate-based optimization for heat exchanger network synthesis and drill scheduling. • We consider essential conditions for robustness to uncertainties and accurate extrapolation. • We investigate two perspectives for developing process engineering surrogates. • For the surrogate-led perspective, we discuss heat exchanger network synthesis. • For the mathematical programming-led approach, we discuss drill scheduling. [ABSTRACT FROM AUTHOR]

Multi-product pipelines usually transport several products in batches to respective delivery stations. As for a multi-product pipeline with multiple pump stations, this paper develops a continuous-time mixed-integer linear programming (MILP) model based on flowrate database to optimize its detailed scheduling. In the proposed model, various unit pump cost and flowrate constraints, which strongly depend on pump operation schemes, are introduced for the economy and safety of solved scheduling plans. Moreover, this paper considers the actual field processing constraints which vary with batch interface migration and rarely considered in previous work. And a novel method of historical flowrate database preprocessing is presented to enhance solving efficiency. Finally, through comparing with three real-world cases solved by another two available models, the proposed one performs the best in scheduling optimization as well as substantial reduction of pump cost. [ABSTRACT FROM AUTHOR]

This paper presents an optimization model of inspection and replacement planning for a refinery plant under the consideration of corrosion in terms of cost. The management of corrosion is an essential task for processes that operate over several years without a shutdown. This is because corrosion can cause severe failures by thinning the wall thickness and eventually cause pipes or equipment to burst. However, required safety measures, such as the corrosion management, involve costly inspection and replacement. Therefore, a cost-effective safety-action strategy is proposed in this paper. The developed model presents an optimal combination of steel grade, design wall thickness, inspection number, and inspection timing under a given corrosion rate to minimize the cost of design, inspection, replacement, and failure. Three case studies using sensitivity analyses are applied to three major processes in a refinery plant: a crude distillation unit, visbreaker, and hydrocracker. [ABSTRACT FROM AUTHOR]

This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance maximizing the revenue stream. This is done by adjusting control inputs like choke valves, artificial lift parameters and routing of well streams. It is well known that the daily production optimization problem is well suited for mathematical optimization. The contribution of this paper is a discussion on appropriate formulations, in particular the use of static models vs. dynamic models. We argue that many important problems can indeed be solved by repetitive use of static models while some problems, in particular related to shale gas systems, require dynamic models to capture key process characteristics. The reason for this is how reservoir dynamics interacts with the dynamics of the production system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a disjunctive formulation for the simultaneous chemical process optimization and heat integration with unclassified process streams –streams that cannot be classified a priori as hot or cold streams and whose final classification depend on the process operating conditions–, variable inlet and outlet temperatures, variable flow rates, isothermal process streams, and the possibility of using different utilities. The paper also presents an extension to allow area estimation assuming vertical heat transfer. The model takes advantage of the disjunctive formulation of the ‘max’ operator to explicitly determine all the ‘kink’ points on the hot and cold balanced composite curves and uses an implicit ordering for determining adjacent points in the balanced composite curves for area estimation. The numerical performance of the proposed approach is illustrated with four case studies. Results show that the novel disjunctive model of the pinch location method has excellent numerical performance, even in large-scale models. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of the steady state economic efficiency of an industrial process is a specific task because the decision variables of the optimization (setpoints of the control system) affect the process through the control strategy. Thus, the effects of saturation of a control system must be taken into account when the gradient of the objective function is estimated and the necessary optimality conditions are checked. In particular, because the optimality conditions cannot be checked directly in the presence of active constraints on the manipulated variables, approximations of the steady state values of the manipulated variables as functions of the setpoints (static plant model) are needed in order to be able to evaluate the optimality conditions. In this paper an iterative method for optimization of the plant profit rate is proposed avoiding the control saturation and is applied to the Pulp Mill benchmark model optimization. Three different static models describing the steady state values of the manipulated variables are constructed and used in the optimization. The results of the optimization are presented and compared against the straightforward single-step optimization of the plant economic efficiency. [ABSTRACT FROM AUTHOR]

• Integrated bioreactor-filtration (primary recovery) design is key in biopharmaceutical manufacturing. • Detailed mathematical modelling and parameterisation of rotational filter behaviour is completed. • Dynamic optimisation of fed-batch and perfusion bioreactors for mAb production is achieved. • Technoeconomic evaluation of optimal integrated designs vs. a variety of parameters is performed. • Fed-batch bioreactors combined with stacked membrane microfilters emerge as industrially optimal. Primary post-cultivation biomass recovery (cell culture removal from API-rich solutions) is essential in biopharmaceutical manufacturing. Centrifugation and depth filtration are dominant industrial primary recovery technologies, but mechanistic dynamic models suitable for performance evaluation are scarce. This paper uses established literature models to present and analyse optimal operation strategies for integrated process designs of fed-batch or perfusion CHO bioreactors (for mAb cultivation via CHO cultures), with an explicit rotational disk (dynamic crossflow) filtration model (for primary recovery). A rigorous DAE filter model (Marke et al., 2020) is employed here, to evaluate system performance. Dynamic optimisation of bioreactor-filter systems has been completed for different bioreactor types, filter arrangements and feed manipulations, considering the same annual mAb plant production target. A technoeconomic analysis of optimal designs addresses industrial viability, confirming a clear cost advantage of fed-batch reactors combined with stacked membrane microfilters. [ABSTRACT FROM AUTHOR]

Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. The goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research. • This paper provides a research overview and trends in expansion planning of power systems. • The expansion planning for power generation and transmission systems is investigated. • Temporal and spatial aggregation, and decomposition for large-scale problems are reviewed. • Reliability, resilience, and flexibility of power systems should be further addressed. [ABSTRACT FROM AUTHOR]

The paper illustrates the redefinition of the concept of biorefinery and its application onto the biogas field. The proposed integrated framework merges in a synergistic way the second and third generation biorefineries with the aim of reducing the CO 2 emissions and maximizing the utilization of biomass potential. The specific focus of the paper is on the mathematical modelling of the main conversion steps of the lignocellulosic residuals into biosyngas by means of thermal treatment (pyrolysis, combustion, gasification) and, next, into biomethanol (heterogeneously catalyzed synthesis). The models are implemented in user-friendly programmes and adopted to estimate biomass conversion and process yield. A final study of sensitivity analysis is provided to open the way for the next process optimization based on detail models. [ABSTRACT FROM AUTHOR]

The paper presents a mixed integer linear programming model which deals with the optimal operation and maintenance of networks of compressors of chemical plants. This optimization model considers condition-based maintenance which involves the degradation of the condition of the compressors. The paper focuses on online and offline washing, two different cleaning procedures which reduce the extra power used by the compressors due to fouling. The state-of-the-art has demonstrated the optimal schedule of the maintenance of a single compressor neglecting the interactions between operation and maintenance of more than one compressor. The suggested optimization model studies a compressor station with multiple compressors and provides their optimal schedule and the best decisions for their washing. Different case scenarios examine the influence of different types of washing methods on the total costs of operation and maintenance. The paper demonstrates the benefits of the optimization and demonstrates that maintenance and operation have to be examined simultaneously and not separately, in contrast to common industrial practice and previous approaches in the literature. [ABSTRACT FROM AUTHOR]

The electrical energy (EE) price plays a significant role in the economic assessment of industrial processes. PSE/CAPE applications are often based on economic evaluations/optimizations where EE price and its possible dynamic evolution are important input data. Planning, scheduling, on-line optimization (dynamic) conceptual design, and feasibility studies are some of the applications where short-, medium-, and long-term predictions of EE quotations are involved. The paper discusses the main issues that contribute to EE quotations such as geographical, meteorological, seasonal, political, social, and financial terms. EE prices show a significant dependency on crude oil quotations with a time-delay of about a quarter/season. An econometric model comprising both linear and periodic components with an implicit stochastic term delivered by the reference component is first commented, then identified, and finally validated respect to real EE quotations. The paper provides also a discussion on how to forecast EE prices ranging from short- to long-term horizons. [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]

• A new deterministic model for WDN optimization is proposed considering unknown flow directions. • The minimum WDN cost model considers a set of available commercial diameters. • No hydraulic simulator is used for calculating pressures and velocities. • Disjunctive programming is used in the model formulation. • Global optimum solutions were obtained. Water Distribution Networks (WDN) are present in a large number of industrial processes and urban centers. Reservoirs, pipes, nodes, loops, and pumps compose WDN and their design can be formulated as an optimization problem. The main objective is the minimization of the network cost, which depends on the pipe diameters and flow directions known a priori. However, in the design of new WDN in real industrial problems, flow directions are unknown. In the present paper, a disjunctive Mixed Integer NonLinear Programming (MINLP) model is proposed for the synthesis of WDN considering unknown flow directions. Two case studies are employed to test the model and global optimization techniques are used in its solution. Results show that the global optima WDN cost with the correct flow directions is obtained for the studied cases without the necessity of using additional software to calculate pressure drops and velocities in the pipes. [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]

Abstract: This paper proposes and explores an algorithm designed to find optimal settings for a process network. Emphasis is put on the system being divisible into components, as this underlying assumption motivates the algorithm in its entirety in that rather simple relations between the system components are modeled as explicit structural constraints, while the significantly more complex relations within each component are approximated based on the underlying simulator data. Although the approach taken in this paper is rather broadly applicable we are, in particular, interested in its application to production optimization problems in the oil and gas industry. We give limited numerical results for one such example that clearly indicates the advantages of our approach. We show the advantages of both decomposing the problem of interest and accounting for the structure from the point of view of exploiting, where ever possible, the explicitly analytic aspects of the problem. The advantage of doing the former is that the considered subproblems are significantly smaller than the overall problem. The advantage of the latter is that one can use derivatives for the analytic parts whereas they are unavailable for the simulators. The underlying approach is a trust-region one with a mixed integer nonlinear program formulation. There are some significant differences in the details of the algorithm from those generally available for such problems. [Copyright &y& Elsevier]

Abstract: The commercialization of cellulosic biofuels has been plagued by multiple sources of market and technology uncertainty, and the lack of capital. It is conjectured that government support for renewable fuels’ has concealed the true market value of its producers, and its removal will lead to a slew of enterprise failures destroying significant private and tax-payer wealth. With such uncertainty, it is prudent to carefully evaluate capital investments in new facilities before actual investment is incurred. This paper presents a strategic optimization model for a multi-product biomass refinery that optimizes the long-term value of the facility to its stakeholders while yielding optimal decisions for input feedstock mix, product portfolio, conversion technology and process configuration, and capital structure for financing. The paper promotes the concept of stakeholder value, where actors at different supply chain nodes are included as a part of a co-operative enterprise, as a means to spread risks across the supply chain. [Copyright &y& Elsevier]

Abstract: In this article, optimization problems with bilinear constraints involving one discrete variable are studied. Several industrial problems present bilinear non-convex constraints which are difficult to solve to global optimality. For this purpose models must be reformulated what in general terms increases the problem size. This article proposes two disjunctive transformation techniques which are compared to other approaches presented in the literature. An analysis is made comparing qualitative and quantitative characteristics of the methods employed. In order to implement proposed transformations, three industrial cases are studied: trim-loss in a paper mill, cutting stock in the production of carton board boxes and the purchase, inventory and delivery optimization problem. All of them are reformulated and solved using the strategies included in the paper. Several instances of each problem are evaluated and their results are analyzed comparing performance of the different methods. [Copyright &y& Elsevier]

Abstract: Dynamic optimization of chemical processes can be carried out with evolutionary algorithms that involve many parameters. These parameters need to be given appropriate values for the algorithms to perform efficiently. This paper proposes parameter setting methods based on factorial experimentation and fuzzy logic, aimed at balancing convergence speed, robustness (consistent performance for each problem) and versatility (applicability to many different problems). The methods were tested on an existing dynamic optimisation method with at least nine tuneable parameters. The test problem set turned out to be quite demanding due to one particular problem behaving in opposite direction to the rest with respect to the most influential factor, population size. It is probable that no single tuning would be possible that will satisfy all problems. However, for the other problems, the Fuzzy Logic tuning method proposed in this paper proves to be a very promising approach. [Copyright &y& Elsevier]

Highlights • MINLP model that represents the stochastic process of system failures and repairs as a continuous-time Markov chain. • Model optimizes the selection of redundancy and the frequency of inspection and maintenance tasks for maximum profit. • Effective decomposition and scenario reduction methods are also proposed t handle large number of states. • Proposed model applied to small and medium scale examples, as well as to air separation process. Abstract This paper proposes an MINLP model that represents the stochastic process of system failures and repairs as a continuous-time Markov chain, based on which it optimizes the selection of redundancy and the frequency of inspection and maintenance tasks for maximum profit. The model explicitly accounts for every possible state of the system. Effective decomposition and scenario reduction methods are also proposed. A small example with two processing stage is solved to demonstrate the impact of incorporating maintenance considerations. A decomposition method and a scenario reduction method are applied to this example and are shown to drastically reduce the computational effort. A larger example with four stages, which is not directly solvable, is also successfully solved using the proposed algorithm. Lastly, we show that the proposed model and algorithms are capable of solving a practical problem based on the air separation process example that motivated our work, which features multiple stages, potential units and failure modes. [ABSTRACT FROM AUTHOR]

Abstract: We address in this paper the optimization of a multi-site, multi-period, and multi-product planning problem with sequence-dependent changeovers, which is modeled as a mixed-integer linear programming (MILP) problem. Industrial instances of this problem require the planning of a number of production and distribution sites over a time span of several months. Temporal and spatial Lagrangean decomposition schemes can be useful for solving these types of large-scale production planning problems. In this paper we present a theoretical result on the relative size of the duality gap of the two decomposition alternatives. We also propose a methodology for exploiting the economic interpretation of the Lagrange multipliers to speed the convergence of numerical algorithms for solving the temporal and spatial Lagrangean duals. The proposed methods are applied to the multi-site multi-period planning problem in order to illustrate their computational effectiveness. [Copyright &y& Elsevier]

Abstract: High performance processes should operate close to design boundaries and specification limits, while still guaranteeing robust performance without design constraint violations. Since design chemical process is operating close to tighter boundaries safely; much attention has been devoted to integrating design and control, in which the design decisions, dynamics, and control performance are considered simultaneously in some optimal fashion. However, rigorous methods for solving design and control simultaneously lead to challenging mathematical formulations which easily become computationally intractable. In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced (). We showed that substantial problem size reduction can be achieved by embedding control for specific process designs. In this paper, we extend the embedded control methodologies to plantwide flowsheet. The case study for the reactor-column flowsheet will demonstrate the current capabilities of the methodology for integrating design and control under uncertainty. [Copyright &y& Elsevier]

Abstract: The paper investigates the periodic operation of a continuous bioreactor described by an unstructured model for which the yield coefficient is linearly dependant on the substrate. The model was studied by a number of authors who showed its ability to predict oscillatory behavior for some range of dilution rates. The paper investigates the different dynamic behaviors predicted by the model when the substrate feed concentration is periodically varied. It is shown that when a region of stable limit cycle is forced with a relatively high forcing frequency, the behavior of the process alternates between periodic and complex chaotic behavior via a number of mechanisms of transition, including period doubling and intermittency. It is shown that the time-average performance of the bioreactor can be substantially improved by an appropriate selection of the forcing amplitude. The effect of the position of the center of forcing on the nature of the emerging patterns are also studied. [Copyright &y& Elsevier]

Highlights • An enhanced surrogate assisted framework for constrained global optimization is proposed. • Maximizing probability of improvement approach is used for selecting infill points. • Kriging meta-models of objective and constraints functions are updated in every iteration. • Meta-model of objective function is local optimized when it's sufficient mature. • Numerical results indicate that the framework is suitable for use in solving computationally expensive and constrained black-box optimization. Abstract An enhanced surrogate assisted framework, based on Probability of Improvement (PI) method, is proposed in this paper. We made some modifications to the original PI approach to enhance the performance of the modeling and optimization framework, leading to fewer rigorous simulations to find the optimal solution without loss of accuracy. We also extended the algorithm for handling general constraints using a fully probabilistic approach. The behavior of the proposed framework was investigated through a set of 9 Unconstrained Test Functions (UTF), 7 Constrained Optimization Problems (COP) and 3 Chemical Engineering Problems (CEP). The numerical results indicate that a lower number of rigorous model simulations were needed for optimizing UTF compared to the classic PI method and that the proposed framework was capable of achieving sustained near optimal solutions for COP and CEP. These results indicate that the proposed framework is suitable for solving computationally expensive constrained black-box optimization problems. [ABSTRACT FROM AUTHOR]

Highlights • Simulation study to economics optimizing control and robust multi-stage optimizing control. • Discussion of numerical aspects of optimizing control as well as the software realization using high-fidelity high-dimensional process models of complex chemical processes. • Control problem formulation for the dynamic product changeover in a multi-product process. • Robustification of economics optimizing control by multi-stage nonlinear model predictive control in case of the presence of model uncertainty. Abstract By the use of online optimization, the profitability of chemical processes can be enhanced while meeting process-related, environmental and ecological constraints. Two well-known techniques to achieve an economically optimal operation of chemical processes are repetitive stationary optimization (RTO) combined with advanced control and direct economics optimizing control on a receding horizon (also called one-layer approach or dynamic RTO). Direct economics optimizing control can react faster to disturbances and is better suited for the optimization of transients between different products. On the other hand it is computationally demanding and requires sufficiently accurate dynamic models. In this paper, we discuss economics optimizing control of a very complex process, a multi-product transesterification reaction that is realized in a reactive distillation process. The process model, the specification of the economics optimizing controller, the implementation of the dynamic optimization, and the robustification of the controller by a multi-stage approach are discussed using simulation studies. [ABSTRACT FROM AUTHOR]

Highlights • A novel piecewise state feedback controller is introduced. • The chemical process optimal control problem is formulated as an optimal control problem of switched dynamical systems. • A gradient-based parallel optimization algorithm is developed for solving the optimal control. • Convergence results indicate that the gradient-based parallel optimization algorithm is global convergent. • Three chemical process optimal control problems are given to illustrate the effectiveness of the proposed algorithm. Abstract This paper considers an optimal control problem of chemical processes with a novel piecewise state-feedback controller. Firstly, the chemical process optimal control problem is formulated as a switched dynamical system optimal control problem, which can be transformed into a parameter optimization problem. Next, to achieve rapid convergence from remote starting points, we propose a novel gradient-based optimization algorithm, which is suitable to a parallel implementation because the step is improved by updating its direction as well as its length simultaneously before moving to the next iteration, and the step computation involves only the inner products of vectors. Then, the convergent properties of the parameter optimization problem to the original optimal control problem are discussed. Finally, the numerical simulation results show that the gradient-based parallel optimization algorithm is an effective alternative method for solving the chemical process optimal control problems. [ABSTRACT FROM AUTHOR]

Highlights • A superstructure of methanation unit is proposed, and the topology of methanation unit is optimized for the coal-to-SNG process. • Simultaneous optimization and heat integration are performed along with the area targeting. • A sequential method is used to provide initial values for the simultaneous model. • Compared with industrial data, the exergy efficiency is improved by 0.77% and TAC can reduce by 10.36 MM$·a−1 for the coal-to-SNG process. Abstract The area of the heat recovery network has a significant impact on the economic performance of the coal-to-SNG process. This paper proposes a superstructure of the methanation unit, and the number of the methanator, recycle gas extraction position (RGEXP), as well as the operating conditions, are optimized. Simultaneous optimization and heat integration are performed along with the area targeting to weigh against the operating cost and capital cost of the coal-to-SNG process. Area cost is evaluated by assuming vertical heat transfer between cold and hot composite curves. A sequential method is used to provide initial values for the simultaneous model. Results show that Case7 (2) has the best economic performance, which has 7 methanators and recycled gas is extracted from the 2nd methanator. The total annual cost of Case7 (2) can be reduced by 10.36 MM$·a−1, and exergy efficiency can be also improved by 0.77% compared with an industrial plant. [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]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

In this paper, we consider near-optimal operation for a class of unconstrained batch processes using the self-optimizing control (SOC) methodology. The existing static SOC approach is extended to the dynamic case by means of a static reformulation of the dynamic optimization problem. However, the dynamic SOC problem is posed as a structure-constrained controlled variable (CV) selection problem, which is different from the static cases. A lower-block triangular structure is specified for the combination matrix, H , to allow for optimal operation whilst respecting causality. A new result is that the structure-constrained SOC problem still results in a convex formulation, which has an analytic solution where the optimal CVs associated with discrete time instants are solved separately. In addition, the inputs are directly determined based on current CV functions for on-line utilization. A fed-batch reactor and a batch distillation column are used to demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

Highlights • We consider Infeasibility resolution for multi-purpose batch process scheduling. • We present a systematic approach for infeasibility diagnosis. • We exploit the known structure of scheduling models to isolate interpretable infeasible sets of constraints. • We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process. Abstract Scheduling decisions give rise to some of the most challenging optimization problems in the process industry. Formulating mathematical models for scheduling problems and devising tailored solution algorithms for these models has been the main thrust of previous literature. In this work, we focus on analyzing and resolving the cause of bottlenecks and infeasibilities in generic scheduling problems. We present a systematic approach for infeasibility diagnosis. Our approach exploits the known structure of scheduling models to isolate interpretable infeasible sets of constraints. We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process modeled using a state-task network (STN) representation. The methodology presented in the paper is able to successfully analyze the cause of infeasibility and provide recommendations for resolving it. We also demonstrate how these insights and recommendations can be presented to scheduling operators with little or no optimization expertise in an intuitive manner. [ABSTRACT FROM AUTHOR]

Highlights • A two phase algorithm for constrained black-box problems is presented. • It does not require a feasible initial point and can handle hard constraints. • Performance compared to NOMAD and COBYLA by applying on large set of problems. • Applied to optimize a process involving nonlinear algebraic and partial differential equations. Abstract This paper presents an algorithm for constrained black-box and grey-box optimization. It is based on surrogate models developed using input-output data in a trust-region framework. Unlike many current methods, the proposed approach does not require feasible initial point and can handle hard constraints via a novel optimization-based constrained sampling scheme. A two-phase strategy is employed, where the first phase involves finding feasible point through minimizing a smooth constraint violation function (feasibility phase). The second phase improves the objective in the feasible region using the solution of the feasibility phase as starting point (optimization phase). The method is applied to solve 92 test problems and the performance is compared with established derivative-free solvers. The two-phase algorithm outperforms these solvers in terms of number of problems solved and number of samples used. We also apply the algorithm to solve a chemical process design problem involving highly-coupled, nonlinear algebraic and partial differential equations. [ABSTRACT FROM AUTHOR]

Abstract: Since plants that form the process network are subjected to fluctuations in product demand or random mechanical failures, design decisions such as adding redundant units and increasing storage between units can increase the flexibility and reliability of an integrated site. In this paper, we develop a bi-criterion optimization model that captures the trade-off between capital investment and process robustness in the design of an integrated site. Design decisions considered are increases in process capacity, introduction of parallel units, and addition of intermediate storage. The mixed-integer linear programming (MILP) formulation proposed in this paper includes the representation of the material levels in the intermediate storage by means of a probabilistic model that captures the effects of the discrete, uncertain events. We also integrate a superstructure optimization with stochastic modeling techniques such as continuous-time Markov chains. The application of the proposed model is illustrated with two example problems. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of extremum-seeking (real-time steady state optimization of dynamic systems via control of the gradient) under inequality constraints in continuous time. A gradient projection framework is adopted in this paper, where the main challenge lies in the identification of the set of active constraints. It is first shown that if the discrete-time switching logic widely used in the literature is directly used in continuous-time, it could cause jamming, i.e., getting stuck in a non-optimal solution. So, an appropriate normalization is proposed to get a jamming-free continuous-time switching logic. The absence of jamming is rigourously proved and is also illustrated on a numerical example. In addition, real-time optimization of the desired product of a reaction taking place in an isothermal continuous stirred-tank reactor is studied, where the gradient of the objective and the constraints are calculated simultaneously using a multi-unit framework. [Copyright &y& Elsevier]

Abstract: The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of Modelica models few efforts have been made in the area of dynamic optimization of Modelica models. In this paper, an extension to the Modelica language, entitled Optimica, is reported. Optimica enables compact and intuitive formulations of optimization problems, static and dynamic, based on Modelica models. The paper also reports a novel Modelica-based open source project, JModelica.org, specifically targeted at dynamic optimization. JModelica.org supports the Optimica extension and offers an open platform based on established technologies, including Python, C, Java and XML. Examples are provided to demonstrate the capabilities of Optimica and JModelica.org. [Copyright &y& Elsevier]

Abstract: The grand challenge facing the power and energy industries is the development of efficient, environmentally friendly, and affordable technologies for next-generation energy systems. To provide solutions for energy and the environment, the U.S. Department of Energy''s (DOE) National Energy Technology Laboratory (NETL) and its research partners in industry and academia are relying increasingly on the use of sophisticated computer-aided process design and optimization tools. In this paper, we describe recent progress toward developing an Advanced Process Engineering Co-Simulator (APECS) for the high-fidelity design, analysis, and optimization of energy plants. The APECS software system combines steady-state process simulation with multiphysics-based equipment simulations, such as those based on computational fluid dynamics (CFD). These co-simulation capabilities enable design engineers to optimize overall process performance with respect to complex thermal and fluid flow phenomena arising in key plant equipment items, such as combustors, gasifiers, turbines, and carbon capture devices. In this paper we review several applications of the APECS co-simulation technology to advanced energy systems, including coal-fired energy plants with carbon capture. This paper also discusses ongoing co-simulation R&D activities and challenges in areas such as CFD-based reduced-order modeling, knowledge management, advanced analysis and optimization, and virtual plant co-simulation. Continued progress in co-simulation technology – through improved integration, solution, and deployment – will have profound positive impacts on the design and optimization of high-efficiency, near-zero emission fossil energy systems. [Copyright &y& Elsevier]

Abstract: Systems technologies emerge with a powerful potential to support the deployment and design of future biorefineries. The chemical industry experiences a steady growth in the use of renewables induced by the gradual depletion of oil, uncertainties in energy supplies and a commanding requirement to reduce GHG emissions and save the planet. Renewables introduce an impressive range of options with biorefining at the center of attention as an emerging industrial concept, uniquely attached to chemical engineering and aiming to transform plant-derived biomass into a variety of products including transport fuels, platform chemicals, polymers, and specialty chemicals. In competing with conventional processes, biorefineries should match maximum efficiencies with better design and process integration. The paper highlights the pivotal role of systems technology to foster innovation, preview options, and support high-throughput computational experimentation, arguing that systems tools are largely under-deployed. Systems-enabled platforms could instead function as powerful environments to generate ideas for integrated designs and offer tremendous services to the complex and large problems produced by the numerous portfolios of feedstocks, unknown portfolios of products, multiple chemistries, and multiple processing paths. Complexities certainly exceed capabilities of previous methodologies but established achievements and experience with similar problems are excellent starting points for future contributions. Besides a general discussion, the paper outlines opportunities for innovation in design, concept-level synthesis, process integration, and the development of supply chains. [Copyright &y& Elsevier]

Abstract: A crucial step for batch process improvement and optimization is to develop information structures that streamline data gathering and, above all, are capable of integrating transactional data into a system using the analytical tools that are developed. Current trends in electronics, computer science, artificial intelligence and control system technology are providing technical capability that greatly facilitates the development of multilevel decision-making support. In this paper, we present the batch process ontology (BaPrOn), wherein different concepts regarding batch processes are categorized and the relationships between them are examined and structured in accordance with ANSI/ISA-88 standards, which provide a solid and transparent framework for integrating batch-related information. This paper also focuses on systematic integration of different actors within the control process. The proposed approach bases the conceptualization through the ANSI/ISA-88 representation, providing the advantage of establishing a more general conceptualization of the batch process domain. The capabilities of the envisaged ontological framework were assessed in a test bed PROCEL pilot plant: scheduling-monitoring and control-rescheduling was closed, information quality was accessed by knowledge description, and an optimum decision-making task was performed. The ontological structure can be extended in the future to incorporate other hierarchical levels and their respective modeling knowledge. [Copyright &y& Elsevier]

Abstract: In this paper we consider the solution methods for mixed-integer linear fractional programming (MILFP) models, which arise in cyclic process scheduling problems. We first discuss convexity properties of MILFP problems, and then investigate the capability of solving MILFP problems with MINLP methods. Dinkelbach''s algorithm is introduced as an efficient method for solving large-scale MILFP problems for which its optimality and convergence properties are established. Extensive computational examples are presented to compare Dinkelbach''s algorithm with various MINLP methods. To illustrate the applications of this algorithm, we consider industrial cyclic scheduling problems for a reaction–separation network and a tissue paper mill with byproduct recycling. These problems are formulated as MILFP models based on the continuous time Resource-Task Network (RTN). The results show that orders of magnitude reduction in CPU times can be achieved when using this algorithm compared to solving the problems with commercial MINLP solvers. [Copyright &y& Elsevier]

Abstract: Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. Three approaches can be distinguished according to the quantities that are adapted: model-parameter adaptation updates the parameters of the process model and repeats the optimization, modifier adaptation modifies the constraints and gradients of the optimization problem and repeats the optimization, while direct input adaptation turns the optimization problem into a feedback control problem and implements optimality via tracking of appropriate controlled variables. This paper argues in favor of modifier adaptation, since it uses a model parameterization and an update criterion that are well tailored to meeting the KKT conditions of optimality. These considerations are illustrated with the real-time optimization of a semi-batch reactor system. [Copyright &y& Elsevier]

In this paper, the problem of how to efficiently operate a natural gas transmission network under steady-state assumptions is considered. The problem is modeled as a nonlinear network optimization problem where the decision variables are mass flow rate in each arc and gas pressure in each node. The objective function to be minimized is the total amount of fuel consumed in the system by the compressor stations. In the past, several techniques ranging from classical gradient-based procedures to dynamic programming, for solving this difficult nonconvex problem have been applied with limited success, particularly when applied to cyclic network topologies. A cyclic system is defined as a network containing at least one cycle involving two or more compressor stations. In this paper we propose a hybrid metaheuristic procedure that efficiently exploits the problem structure. This hybrid procedure combines very effectively a nonsequential dynamic programming algorithm for finding an optimal set of pressure variables for a fixed set of mass flow rate varables, and short-term memory tabu search procedure for guiding the search in the flow variable space. The proposed procedure represents an improvement to the best existing approach to the best of our knowledge. In addition, empirical evidence over a number of instances supports the effectiviness of the proposed procedure outperforming a multi-start GRG method both in terms of solution quality and feasibility. Furthermore, to assess the quality of the solutions obtained by the algorithm, a lower bound is derived. It is found that the solution quality obtained by the proposed procedure is relatively good. [Copyright &y& Elsevier]

Abstract: In the present work, the planning and cutting problem for the corrugated board boxes industries is presented. This problem belongs to the category of the trim-loss problem, which is essential in the paper-converting supply chain management. Bilinear terms in demand and stock constraints, for instance, lead to a non-convex formulation. Two global convex models are formulated and tested. Results obtained in the problem solution are shown. The most efficient model is implemented by means of Java programs and GAMS, a mathematical optimization program. The system is linked to the company ERP (enterprise resource planning) system. Several issues are optimized and improved: waste generation, energy demand, environmental impact and production costs. Paper reel stock management is improved due to more accurate and statistical information obtained by the system. The planning system linked to the ERP connection allows the integration of customers and suppliers increasing the company competitiveness. [Copyright &y& Elsevier]

This paper presents a general mathematical optimization model and the related deterministic algorithm for synthesizing heat-exchanger networks (HEN). The methodologies commonly used to solve a HEN problem are based on heuristic methods. These algorithms reach a presumed best solution with respect to the investigated set of process streams, whereas our approach determines the optimal configuration. In this paper the proposed method is applied to the solution of a few simple HEN synthesis problems and the results are compared with those obtained utilizing heuristic or meta-heuristic methods. HEN''s problem is represented by a general mathematical model that can be applied to small and medium sized problems and is solved by using a deterministic method with a reasonably brief computation time. Notably, this approach can represent a unique general model of a wide class of heat-exchanger networks, independently of their specific physical configuration. [Copyright &y& Elsevier]

In this paper, the distribution of off-gas used as fuel by many different plant units in an iron and titanium plant, is studied. An optimization algorithm is applied to find the operating strategy leading to the best use of available off-gas by the different consumers. This is realized by adjusting the flow of the material through different kilns and the stock piles. In addition, the choice of whether or not to operate a certain kiln is treated as a decision variable. The contribution of this paper lies in formulating a linear model describing the system and solving the above mentioned problem as a mixed integer optimization problem in real time. A simulation result is shown and explained. [Copyright &y& Elsevier]

Abstract: There has been significant progress in the area of short-term scheduling of batch processes, including the solution of industrial-sized problems, in the last 20 years. The main goal of this paper is to provide an up-to-date review of the state-of-the-art in this challenging area. Main features, strengths and limitations of existing modeling and optimization techniques as well as other available major solution methods are examined through this paper. We first present a general classification for scheduling problems of batch processes as well as for the corresponding optimization models. Subsequently, the modeling of representative optimization approaches for the different problem types are introduced in detail, focusing on both discrete and continuous time models. A comparison of effectiveness and efficiency of these models is given for two benchmarking examples from the literature. We also discuss two real-world applications of scheduling problems that cannot be readily accommodated using existing methods. For the sake of completeness, other alternative solution methods applied in the field of scheduling are also reviewed, followed by a discussion related to solving large-scale problems through rigorous optimization approaches. Finally, we list available academic and commercial software, and briefly address the issue of rescheduling capabilities of the various optimization approaches as well as important extensions that go beyond short-term batch scheduling. [Copyright &y& Elsevier]

Abstract: This paper addresses the integrated planning/scheduling of chemical supply chains (SC) with multi-product, multi-echelon distribution networks taking into account financial management issues and suggests a novel approach for enterprise wide management. In order to tackle this problem, it is derived a mathematical formulation combining a scheduling/planning model with a cash flow and budgeting formulation. To motivate the use of such integrated model, a sequential scheme representing traditional enterprise practices is firstly applied. Within this strategy, scheduling and planning decisions are taken firstly, and finances are fitted afterwards considering the cash flows associated to the scheduling/planning decisions previously computed as input parameters. The comparison between the results of the sequential approach and those of the integrated model highlight the advantages of the latter option, in which scheduling/planning and cash management decisions are optimized in unison with a common objective of maximizing the change in equity achieved by the company. The modeling approach developed in this paper and the obtained results suggest that a new conceptual strategy in enterprise management systems consisting of the integration of the financial models of the enterprise with those dealing with the operative area is a must to improve the firm’s performance and its overall earnings and ensuring also healthy cash flow management. [Copyright &y& Elsevier]