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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]

This paper presents computer-based design and analysis of control systems for centrifugal compressors when the operating fluid is supercritical CO 2 . It reports a non-linear dynamic model including a main forward compression line and two different configurations for the recycle antisurge line. Disturbance scenarios are proposed for testing the configurations and performance indicators are suggested to evaluate control performance and power consumption of the compression system. The paper demonstrates that compared to the hot recycle, the process configuration including a cold gas recycle has better overall stability, but higher power consumption and lower values for the control performance indicators. Based on the previous considerations, the paper gives suggestions regarding the choice of the recycle configuration. Moreover it compares subcritical and supercritical compression during surge prevention and highlights the importance of the selection of the gas recycle configuration when full recycle is needed. [ABSTRACT FROM AUTHOR]

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]

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]

Graphical abstract Image, graphical abstract Highlights • A two-dimensional, two-phase, isothermal model is presented for PEMFCs. • The modeling results agree well with the different experimental data. • Effects of cell voltage on the species distribution are explored. • Effects of cell voltage on the saturation level distribution are studied. Abstract In this paper, a two-dimensional, two-phase, isothermal model is presented to investigate the water transport characteristic and water distribution in a proton exchange membrane fuel cell (PEMFC) with emphasis on exploring the water distribution in the layers of PEMFC. The governing equations have been solved over a single computational domain, which consists of all the layers. The result of polarization curve has been validated against the experimental data in an extensive range of operating conditions. A parametric study has also been performed to examine the effects of the diffusion layer (DL) permeability, contact angle, and cell output voltage on water distribution in the various layers of PEMFC. Numerical results elucidate that water distribution level in the cathode side is decreased with the increment in the cell voltage. Furthermore, the permeability of the cathode DL and contact angle significantly influence the water saturation level in the cathode side. [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]

Using sequence information can improve performances in fault detection for serial (temporal) correlated process data. Classical methods firstly construct extended vectors through concatenating current process data and a certain number of previous process data, and then take dimension reduction methods. However, the simple extension of process data may distort the correlation between variables and largely increase the dimensionality. This paper proposes a novel algorithm, called Dynamic Graph Embedding (DGE), for fault detection. DGE adopts augmented matrices instead of extended vectors to encode sequence information. Furthermore, DGE incorporates both time information and neighborhood information to form similarities of different process data. And then DGE is designed to obtain embedding matrices with Markov chain analysis of the similarities. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of DGE in terms of missed detection rate (MDR) and false alarm rate (FAR). [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, a new methodology has been developed to deal with the front-end crude scheduling (FECS) problems with the consideration of inherent upset minimization (IUM). Specifically, the primary and secondary inherent upsets (IUs) have been defined and modeled to, respectively, address flowrate fluctuations of feeding crude distillation units and the long-distance pipeline (LDPL). Based on such IU characterizations, a new MINLP model with the unit-based continuous time representation is developed to determine the optimal FECS solution by minimizing the overall operating cost and instability along the entire scheduling time horizon. In addition to the IUM, another two merits are also included in this study: (i) the trans-mixing (TM) issue along with crude transportation inside the LDPL has been modeled; (ii) multiple types of crudes with multiple key properties have been simultaneously considered. The developed FECS MINLP model is solved by the ANTIGONE solver to obtain the global optima. The efficacy of the proposed methodology and the FECS model, and the effect of IUM have been investigated through various case studies. [ABSTRACT FROM AUTHOR]

This paper proposes a locality preserving discriminative canonical variate analysis (LP-DCVA) scheme for fault diagnosis. The LP-DCVA method provides a set of optimal projection vectors that simultaneously maximizes the within-class mutual canonical correlations, minimizes the between-class mutual canonical correlations, and preserves the local structures present in the data. This method inherits the strength of canonical variate analysis (CVA) in handling high-dimensional data with serial correlations and the advantages of Fisher discriminant analysis (FDA) in pattern classification. Moreover, the incorporation of locality preserving projection (LPP) in this method makes it suitable for dealing with nonlinearities in the form of local manifolds in the data. The solution to the proposed approach is formulated as a generalized eigenvalue problem. The effectiveness of the proposed approach for fault classification is verified by the Tennessee Eastman process. Simulation results show that the LP-DCVA method outperforms the FDA, dynamic FDA (DFDA), CVA-FDA, and localized DFDA (L-DFDA) approaches in fault diagnosis. [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]

Abstract: Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process. [Copyright &y& Elsevier]

Reaction network model is central to ethylene cracking process simulation. Studying an ethylene cracking reaction (ECR) network, which involves hundreds of components and thousands of reactions, becomes a difficult task. To facilitate a rapid and comprehensive reaction network analysis and improve ECR network, this paper introduced a ranking algorithm called network flow analysis algorithm (NFAA) to a reaction network analysis procedure. NFAA analyses the reaction network with comprehensive information such as network topological structure, reaction mechanism, and process model data. Following NFAA, reactions and species are ranked based on their significances. According to the ranking of reactions, unimportant reactions (lower ranking) in ECR network are removed to reduce ECR network complexity and decrease computational scale without loss of prediction accuracy. On the other hand, rankings provide guidance on adjusting parameters of ECR network. The application of NFAA makes a progress in improvement of ECR reaction network in an industrial case. [ABSTRACT FROM AUTHOR]

For many industrial sites, a production scheduling solution – be it manual or optimized – is in many ways still a complex and “exotic” application that seldomly finds its way into the daily practice of the plant floor. As it lies within the interface between business and control systems, scheduling is too often seen as something decoupled and theoretical that requires high-level experts for configuring, using and maintaining it. In this paper, we discuss some of the challenges and hurdles for deploying scheduling solutions. A relevant ongoing technological transition towards stronger integration is also addressed. Some potential for improvements are identified both in academia and industry. By addressing the main pain points it can be seen that with a common mind-set the complexity of deploying a scheduling solution can be lowered significantly. [ABSTRACT FROM AUTHOR]

Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM. [ABSTRACT FROM AUTHOR]

Opportunities and challenges in the petrochemical industry and the emergence of massive disruptive technologies have triggered a new revolution that has the power to fundamentally change industrial processes including manufacturing, engineering, materials, supply chains, lifecycle management. Recently, the newly arisen smart factory adopted a disruptive manufacturing methodology and has become a key part of the petrochemical industry. The smart factory, which is different from the original production systems used in the petrochemical industry, needs to assess and position its future research agenda including its definition, intension, framework, and technology. Systems thinking and systems problem solving for the smart factory must be prioritized. Based on an analysis of the driving force for smart factory development, this paper proposes a lifecycle blueprint and consensus-based operating and technology roadmap. The definition and features of a smart factory in the petrochemical industry are presented. Furthermore, a summary of the technical systems and future-proof research field of the smart petrochemical factory from an academic and industrial viewpoint is presented. [ABSTRACT FROM AUTHOR]

This paper deals with time-optimal operation of batch diafiltration processes in the presence of membrane fouling. Fouling causes a decrease in the effective membrane area and, hence, an increase in processing time. In this work we study a time-optimal operation with several fouling models available in literature. Pontryagin's minimum principle is applied to characterize the structure of the optimal operation which voids any further on-line optimization. Due to the specific structure of the problem, it is possible, in several problem setups, to derive and verify an explicit analytic solution. Obtained results are applied in case studies where we provide a comparison between traditional operation and the proposed time-optimal operation. In cases, where the optimal operation cannot be identified analytically, we analyze the performance of sub-optimal control derived from neighboring analytical solution and compare it to optimal operation found via numerical optimization techniques. [ABSTRACT FROM AUTHOR]

In model-based real-time optimization, plant-model mismatch can be handled by applying bias- and gradient-corrections to the cost and constraint functions in an iterative optimization procedure. One of the major challenges in practice is the estimation of the plant gradients from noisy measurement data, in particular for several optimization variables. In this paper we propose a new real-time optimization scheme that explores the inherent smoothness of the plant mapping to enable a reliable optimization. The idea here is to combine the quadratic approximation approach used in derivative-free optimization techniques with the iterative gradient-modification optimization scheme. The convergence of the scheme is analyzed. Simulation studies for the optimization of a ten-variable synthetic example and a reactor benchmark problem with considerable plant-model mismatch show its promising performance. [ABSTRACT FROM AUTHOR]

In this paper Multistage flash (MSF), Reverse Osmosis (RO) and hybrid MSF/RO desalination systems are optimized. A superstructure is set up for analyzing various process configurations in a single flowsheet. Detailed steady state models for MSF and RO sections of the superstructure are developed incorporating comprehensive physio-chemical properties and design characteristics. The model for the hybrid system combines individual models of MSF and RO systems and additional separators and mixers. The optimization variables consist of the operating and design variables, and the objective function is developed on the basis of economic and technical performance indicators. Primary results show that the hybridization of MSF and RO systems sharing common intake-outfall facilities presents lower cost with the additional benefits of higher overall recovery than MSF system and higher product quality than RO system. The sensitivity analysis of the cost parameters is performed to realize their effects on the selection of process configuration. [ABSTRACT FROM AUTHOR]

In this first of a series of two papers, the effect of varying steam levels on the total steam flowrate within steam systems is analysed mathematically for the traditional parallel configuration as well as for the case of hot liquid reuse. It is demonstrated that in the case of parallel heat exchangers utilising only latent heat, a minimum total steam flowrate is obtained by optimally selecting steam levels, but that in the case of hot liquid reuse, introducing multiple steam levels increases the minimum total steam flowrate attainable under those conditions. The flowrate attained utilising hot liquid reuse, however, remains lower than when only utilising latent heat. It is concluded that the lowest steam flowrate is attained using hot liquid reuse and only a single level of steam, but that the presence of additional steam levels resulting from turbines requires a more holistic approach to the synthesis of steam networks. [ABSTRACT FROM AUTHOR]

Presented in this paper is a mathematical technique for simultaneous heat integration and process scheduling in multipurpose batch plants. Taking advantage of the intermittent continuous behavior of process streams during transfer from one processing unit to another, as determined by the recipe, the presented formulation aims to maximize the coincidence of availability of hot and cold stream pairs with feasible temperature driving forces, while taking into consideration process scheduling constraints. Contrary to similar contributions in published literature, time is treated as one of the key optimization variables instead of a parameter fixed a priori. Heat integration during stream transfer has the added benefit of shortened processing time, which invariably improves throughput, as more batches are likely to be processed within a given time horizon, compared to conventional heating and cooling in situ. Application of the proposed model to a case study shows improvements of more than 30% in energy savings and up to 15% in product output. [ABSTRACT FROM AUTHOR]

This paper provides a historical perspective and an overview of the pioneering work that Manfred Morari developed in the area of resiliency for chemical processes. Motivated by unique counter-intuitive examples, we present a review of the early mathematical formulations and solution methods developed by Grossmann and co-workers for quantifying Static Resiliency (Flexibility). We also give a brief overview of some of the seminal ideas by Manfred Morari and co-workers in the area of Dynamic Resiliency. Finally, we provide a review of some of the recent developments that have taken place since that early work. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new approach for performing feasibility analysis over a multivariate factor space when the explicit form of a process model is lacking or when its evaluation is expensive. Specifically, two issues are addressed: feasibility evaluation of black-box processes using Kriging and development of an adaptive sampling strategy in order to minimize sampling cost, while maintaining feasibility space accuracy. Kriging is chosen as the interpolating technique for constructing a response surface of the feasibility function as a function of the uncertain parameters when a set of input–output data are available. The adaptive sampling strategy identifies critical regions and directs the search towards feasibility boundaries or where the Kriging prediction uncertainty is high. The average Kriging prediction error and cross-validation methods are used to validate the robustness of the produced model of the initial experimental design which is found to highly affect the final predicted feasible region. [Copyright &y& Elsevier]

Abstract: The interest on renewable fuels has greatly increased in the last years. Particularly, ethanol production arises as a good solution to many current economic-environmental problems. Yeast production from the ethanol residuals constitutes a sustainable alternative. Usually, this kind of plants is designed using single product campaigns. However, since yeast degradation is fast and a continuous supply must be assured, the mixed product campaign policy is the most appropriate. Besides, a stable context can be assumed to justify this approach that takes advantage of the special structure of the plant. Therefore, in this paper, a mixed integer linear programming model is formulated for simultaneous design and scheduling of a semicontinuous/batch plant for ethanol and derivatives production. The optimal plant configuration, unit sizes, number of batches of each product in the campaign and its sequencing is obtained in order to fulfill the ethanol and yeast demands minimizing the investment cost. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new cutting plane algorithm which is computationally less expensive and more efficient than Kelley’s algorithm. This new cutting plane algorithm uses an intersection cut of three types of cutting planes. We find from numerical results that the global search method formed using successive linear programming and a new intersection set is at least twice as fast as Kelley’s cutting planes. The necessary mathematical analysis and convergence theorem are provided. The key findings are illustrated via optimization of a cascade of three CSTRs. [Copyright &y& Elsevier]

Abstract: This work presents a new plant-wide control strategy able to be applied on large scale chemical plants. It is based on an extension of the non square relative gain array (NRG) theoretical concepts, introduced by Chang and Yu (1990), and the generalized relative disturbance gain (GRDG) presented in Chang and Yu (1992). The extension of the NRG is useful for searching the best group of controlled variables (CVs) independently of the problem dimensionality. Meanwhile, the extension of the GRDG allows configure the loops pairing by considering the trade-off between servo and regulator behavior. It can be done thanks to define a proper function, named net load effect, accounting both set point and disturbances effects. Even though these concepts are not new, the main contribution of this paper is the selection of the adequate objective function. It is mathematically expressed in a new way, in terms of Frobenius norm of specific matrices related with the models of the plant and very useful for evaluating the process interaction. Then, it drives the search supported by genetic algorithms (GA), which evaluates all the possible combinations of input–output variables. It allows to solve successfully and with less computational effort the combinatorial optimization problem, even though the high dimension usually involved in large scale chemical plants. The use of the relative gain array (RGA) can also be considered for pairing purpose, but in some cases it could drive to a less effective structure. The use of relative normalized gain array (RNGA) for pairing the selected CVs with the most suitable MVs is able to lead to best control structures only if a dynamic model of the plant is available. Therefore, it must be emphasized that this approach is developed for working in cases where only steady-state plant information is available. However, if a dynamic model is disposable too the algorithm is extended to use it. In addition, a mathematical demonstration is presented so as to understand why is possible to find a well conditioned control structure. The methodology is tested in the Tennessee Eastman (TE) process at the base case proposed by Downs and Vogel (1992), and at an optimized working point presented by Ricker (1995). Both working points show two quite different scenarios. Thus, a set of dynamic simulations for both cases and the hardware requirements compared to the previous suggested are given to proof the capacity of this approach. [Copyright &y& Elsevier]

Abstract: This paper proposes an initialization approach for parameter estimation problems (PEPs) involving parameter-affine dynamic models. By using the state measurements, the nonconvex PEP is modified such that a convex approximation to the original PEP is obtained. The modified problem is solved by convex optimization methods yielding an approximate solution to the original PEP. The approximate solution can be further refined by linearizing the original problem around the obtained minimum. An assessment of the distance between the real solution and the one provided by the linearization of the problem around the convex approximation is presented. The optimum obtained by the convex approximation is used to subsequently initialize a simultaneous Gauss–Newton (SGN) approach on the original nonconvex PEP. Comparative results for the SGN with arbitrary initialization and with the proposed approach are presented using three benchmark examples in the chemical and biological fields. [Copyright &y& Elsevier]

Abstract: In this paper, we present recent developments in multiparametric nonlinear programming. For the case of convex problems, we highlight key issues regarding the full characterization of the parametric solution space and we discuss, through an illustrative example problem, four alternative state-of-the-art multiparametric nonlinear programming algorithms. We also identify a number of main challenges for the non-convex case and highlight future research directions. [Copyright &y& Elsevier]

Abstract: Reliability and optimal sensor design are of great interest for control engineers and the researcher community. The severity of faults is addressed through redundancy analysis. A structural analysis is introduced which uses symbolic calculus to determine which redundancy exists in the system and provides the means to find the particular sensor defaults. The paper is organized as follows. The first part gives an overview on Groëbner bases and their programming. Definitions are included to specify explicitly what should be understood by the terms “symbolic calculus software” and more specifically lexicographic programming and Groëbner base algorithms. The second part develops the concept of observability bases and in the third part we show how to use these bases to make optimal and reliable design. The biggest benefit of this method is the ability to extend the mathematical process to the n-linear case. [Copyright &y& Elsevier]

Abstract: The focus of this paper is the numerical study of vapor phase-diffusion driven sessile drop evaporation to study the evaporation flux profile along the interface of an evaporating sessile drop and its effect on the fluid dynamics inside the drop. The spatial variation of vapor phase concentration of the evaporating liquid is governed by the Laplace equation and the fluid dynamics is governed by Stokes equation. In this study, the numerical analysis and the mesh discretization method are described thoroughly. The time dependent evaporation dynamics, drop shape, and fluid dynamics are solved simultaneously. We compared the calculated velocity profiles to the result presented in the previous work [Widjaja, E., Liu, N., Li, M., Collins, R. T., Basaran, O. A., & Harris, M. T. (2007). Computers and Chemical Engineering, 31(3), 219–232] in the case of uniform evaporation flux along the drop interface. The two cases gave different results, which confirmed that fluid velocity profiles inside the drop are influenced by the evaporation flux profiles along the drop interface. [Copyright &y& Elsevier]

Abstract: This paper addresses the application of Genetic Programming (GP) to the synthesis of heat-integrated complex distillation system and the flowsheet of complex separation can be expressed directly using GP''s special hierarchical structure. A series of unique encoding method and solution strategy is proposed and some evolutionary factor is improved based on the domain knowledge of chemical engineering. A shortcut method is applied to calculate all required design parameters. Conventional and complex columns, thermally coupled (linked) side strippers and side rectifiers, fully thermally coupled columns as well as heat integration between any different columns are simultaneously considered. Two illustrating examples are presented to demonstrate the effective computational strategies. [Copyright &y& Elsevier]

Abstract: This paper is the first part of a two-part manuscript in which we propose a new equation-based approach to process flowsheeting. In part I, a simultaneous linearization algorithm to solve problems for inter-linked, multicolumn separation processes is presented. The new algorithm is an extension of our method for single-column problems based on the Pseudo-Binary-Mixture (PBM) concept [Ishii, Y., & Otto, F. D. (2001). An efficient simultaneous correction procedure for multi-component, multistage separation calculations for non-ideal systems. Computers and Chemical Engineering, 25, 1285–1298]. The PBM-based algorithm is capable of handling any flowsheet topology regardless of the number and form of recycle streams and is characterized by a greatly simplified matrix structure and the ability to solve complex EO flowsheeting problems with the same ease as for a single-column problem. Three example problems that have a large number of recycle steams demonstrate the robustness of the algorithm and as well as the flexibility and efficiency of EO flowsheeting. In part II the algorithm is extended for general-purpose process flowsheeting, including the ability to solve controlled simulation problems where design specifications are imposed and the ability to solve process optimization problems. As well, a new local thermodynamic interface to provide the derivative information required in EO flowsheeting is presented. The methodologies presented in this work successfully address many of the difficulties associated with current EO flowsheeting technology. [Copyright &y& Elsevier]

Abstract: Applications for on-line data reconciliation and optimization must be efficient and numerically robust. The models in these applications are rarely changed and the same optimization problem is solved thousands of times with only minor changes in the parameters. This paper describes a suitable modeling framework for this type of applications that, with the aim of simplifying the creation of new models, makes the application robust and avoids numerical difficulties. The model is based on a unit model structure where first-order derivatives, scaling and initial values are properties of the unit model. A new scaling procedure is proposed based on equation and variable pairing. The modeling framework and the use of the proposed scaling procedure are demonstrated in two case studies, case 1 is simulation of a simple pipe model, case 2 is simulation, data reconciliation and optimization of a flash process. [Copyright &y& Elsevier]

Abstract: Multi-parametric model-based control (mp-MPC) is a control method that is widely acknowledged for its ability to solve the on-line optimisation problem, involved in traditional MPC, off-line via parametric optimisation. Its main advantage is that it obtains the control actions as explicit functions of the plant measurements. This allows for the control actions to be obtained on-line via simple function evaluations instead of solving repetitively a computationally demanding on-line optimisation. This allows mp-MPC to be implemented on the simplest, low-cost hardware. In this paper we report on recent developments on industrial and experimental applications of mp-MPC, where the ability of mp-MPC to be applied on systems with fast dynamics and sampling times is demonstrated, which maybe prohibitive for traditional MPC that relies on on-line optimisation methods. [Copyright &y& Elsevier]

Abstract: This paper presents a general multiperiod optimization model, which simultaneously solves the design and planning decisions in multiproduct batch plants. Therefore, the trade-offs between both problems are taken into account as well as variations due to seasonal effects, demand patterns, etc. From the design point of view, the model is formulated considering batch and semicontinuous units, the allocation of intermediate storage, and structural decisions. Following the usual procurement policy, equipment is provided using discrete sizes. From the planning point of view, the formulation takes into account both products and raw materials inventories, product demands and raw materials supplies that vary seasonally in a multiperiod approach. The objective is the maximization of an economic function, which considers incomes, and both investment and operation costs. A plant that produces five oleoresins in seven stages is used to illustrate this approach. [Copyright &y& Elsevier]

Selecting an appropriate path for acquiring commercial-scale biopharmaceutical manufacturing capacity often requires the rationalisation of multiple conflicting criteria and the reconciling of financial and non-financial issues. Multi-criteria decision-making (MCDM) can provide a holistic framework for evaluating such scenarios. This paper presents the development of a decision-support framework for decision-making scenarios involving the acquisition of commercial-scale biopharmaceutical manufacturing capacity that utilises MCDM. To illustrate the functionality of the framework, a hypothetical scenario was constructed based on a biopharmaceutical company faced with a number of options for acquiring commercial manufacturing capacity. A deterministic analysis showed that building manufacturing capacity was the highest ranking option for the scenario considered. Stochastic analyses demonstrated that this option was also the highest ranking overall. [Copyright &y& Elsevier]

This paper proposes a visualization method for finding the optimal operating region and a near-optimal operating point based on process data. The basic principle behind the method is to map data in multi-dimensional space to a two-dimensional plane, and to generate simultaneously the contours of the objective function or functions with a mapping model. The optimal operating region can be located intuitively according to the contour distributions of the objective functions and a point found which is in this region and although not strictly optimal is near-optimal. The optimal point found can be mapped back to the original multidimensional space with a reverse mapping method proposed previously. The visualization method is tested on four operating optimization problems, including problems with multiple objective functions. Results show that the proposed method is able to reveal fully the features of the operating space and to find effectively the optimal operation region and a good operating point. [Copyright &y& Elsevier]

Abstract: Zero-wait (ZW) is a special type of batch operation in which products are processed without being stored in order to produce a number of low volume high value-added chemical products. Because of its economic impact, there have been a number of studies on the scheduling of ZW processes. However, they are mainly focusing on formulating it into mathematical optimization problems assuming deterministic information. In reality, parameters in the ZW scheduling problem are subject to variation, which may make a fixed schedule suboptimal or even infeasible. Therefore, the scheduling problem has to be solved over and over again using the varying parameters. In order to overcome the inefficiency of such repeated computations, this paper introduces parametric programming technique for solving the ZW scheduling problem under uncertainty. The main advantage using the proposed technique is that a complete map of optimal schedules is obtained as a simple function of varying parameters. A new optimal schedule is thus obtained as a simple function evaluation instead of additional resource-expensive optimization computations. Computational experience with the proposed model and algorithm is presented in the form of two numerical examples. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to present an approach to express certain types of optimal control problems in terms of a system of differential algebraic equations (DAEs). This system is obtained using calculus of variations to get the Kuhn–Tucker conditions. The inequalities associated with the complementarity conditions are converted to equalities by the addition of a new variable. Such systems of DAEs are well known in the Chemical Engineering literature, and there are a number of established numerical methods for their solution. Also, we introduce here the concept of the tractability index as a general purpose way of determining the index, by establishing which part of the system of DAEs must be differentiated and how many times. This provides a systematic way of determining the index, without needing to differentiate the whole system. Numerical examples from Chemical Engineering are used to illustrate the methodology. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a dynamic real-time optimization (RTO) strategy for an integrated plant. The steady-state assumption in conventional RTOs severely limits frequency of optimization and precludes the use of dynamic degrees of freedom available in the plant (e.g. storage capacities), resulting in suboptimal economic performances. Other approaches that attempt to synchronize the frequency of the plant-level optimization to that of the local-unit-level model predictive controllers can be sensitive to local disturbances and model uncertainty in high-frequency parts of plant dynamics. We reason that a logical middle ground is to perform a dynamic optimization but at a rate significantly lower than the model predictive controllers in order to keep the modeling and computational requirements at a reasonable level for the optimization. We discuss the obtaining of a reduced-order model for a chosen optimization frequency and the interfacing of the real-time optimizer with unit controllers. Two examples are given to compare the various approaches. [Copyright &y& Elsevier]

Abstract: The paper presents the extension of our work on the adaptive steady-state simulation of refinery processes to dynamic problems. The results show that the adaptive algorithm is capable of following the changes in the composition by adapting the composition representation during the course of simulation. The method is based on a continuous model formulation. In principle, each model given in a discrete (or pseudocomponent) formulation can be rewritten by means of continuous distribution functions. This continuous formulation does not yet reduce the model size but comprises the full detailed model. A model reduction is accomplished by applying a discretization scheme to the continuous formulation. Thus, the continuous reformulation allows the usage of modern discretization techniques including grid adaptation (adaptive wavelet-Galerkin discretization) and multigrid methods. In the iterative algorithm suggested, the approximate solution of the dynamic process model is obtained on the full composition grid. In each iteration loop the current iterate is corrected by a correction term, which is determined on a coarse grid for the composition representation. According to the multigrid framework the obtained intermediate solution is prolongated to the full composition detail by means of a linearization of the model equations. Within these iterations, the discretization grid is adapted by exploitation of the particular properties of the employed wavelet basis functions. [Copyright &y& Elsevier]

It is now recognized that model predictive control (MPC) is an interesting alternative for real-time control of industrial processes. In the meantime, some problems do still remain in progress: for theoretical aspects, the a priori guarantee of the stability and for the practical aspects, the guarantee of sufficient time to solve to optimization problem at each sampled time positions. In this paper, we propose a global method that aims to reduce the on-line calculation time due to the PDE model based optimization task resolution. It is addressed for a particular class of systems not very often studied in this context: systems described by partial differential equations (PDEs) which are, in the present case, nonlinear and parabolic. In order to decrease the computational burden, the nonlinear PDE system is solved off-line. Then, a linearized PDE model around the previous off-line behavior is used to find the optimal variations for the on-line predictive control. The real-time control application given is concerned with a infrared drying process of painting film. [Copyright &y& Elsevier]