Journal Sciences News
Veterinary Clinics of North America: Equine Practice
12 July 2018
Fault detection and diagnosis using empirical mode decomposition based principal component analysis
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Yuncheng Du, Dongping Du This paper presents a new algorithm to identify and diagnose stochastic faults in Tennessee Eastman (TE) process. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) to diagnose a group of faults that could not be properly detected and/or diagnosed with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, fault detection, and fault diagnosis. Measured variables are first decomposed into different scales using the EEMD-based PCA, from which fault signatures can be extracted for fault detection and diagnosis (FDD). The T 2 and Q statistics-based CUSUMs are further applied to improve fault detection, where a set of PCA models are developed from historical data to characterize anomalous fingerprints that are correlated with each fault for accurate fault diagnosis. The algorithm developed in this paper can successfully identify and diagnose both individual and simultaneous occurrences of stochastic faults.
12 July 2018
Distributed fault diagnosis for networked nonlinear uncertain systems
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Hadi Shahnazari, Prashant Mhaskar In this work, we address the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems utilizing a distributed fault detection and isolation (FDI) strategy. The key idea is to design a bank of local FDI (LFDI) schemes that communicate with each other for improved FDI. The proposed distributed FDI scheme is shown to be able to handle local faults as well as those that affect more than one subsystem. This is achieved via appropriate adaptation of the LFDI filters based on information exchange with other subsystems and using the proposed notion of detectability index. The detectability index and isolability conditions are rigorously derived for the distributed FDI scheme. Effectiveness of the proposed methodology is shown via application to a reactor-separator process subject to uncertainty and measurement noise.
12 July 2018
Steady-state real-time optimization using transient measurements
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Dinesh Krishnamoorthy, Bjarne Foss, Sigurd Skogestad Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a “hybrid” approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations.
12 July 2018
Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Melis Onel, Chris A. Kieslich, Yannis A. Guzman, Christodoulos A. Floudas, Efstratios N. Pistikopoulos This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark data set which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the pre-aligned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
12 July 2018
A methodology to reduce the computational cost of transient multiphysics simulations for waste vitrification
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Alexander W. Abboud, Donna Post Guillen Legacy radioactive waste stored in tanks at the Hanford Site is scheduled to undergo vitrification in Joule-heated melters. A carefully calibrated computational fluid dynamics model has been developed to characterize fluid flow, chemistry and heat transfer in the melters. Bubbling is replaced by momentum source terms to approximate forced convection circulation patterns and reduce Courant number restrictions on the resolved liquid–air interface. Void zones in the electrical field compensate for the removal of bubbles. The efficiency of the radiation solver is improved by reducing the update frequency of the discrete ordinates and using lower quadrature. A simple polynomial fit captures the waste-to-glass reactions in the cold cap. These simplifications reduce the turnaround time such that it is possible to simulate hundreds of seconds of physical time per day with the calibrated model versus only several seconds of physical time with the original, higher-fidelity model.

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12 July 2018
A multistream heat exchanger model with enthalpy feasibility
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Kyungjae Tak, Hweeung Kwon, Jaedeuk Park, Jae Hyun Cho, Il Moon A temperature feasibility constraint is an important part of multistream heat exchanger (MSHE) modeling. However, temperature feasibility of an MSHE model makes a numerical issue when a physical property package is used to obtain highly accurate temperature-enthalpy relationships in equation-oriented modeling environment. To resolve the issue, this study proposes a new MSHE model with enthalpy feasibility using the fact that enthalpy is a monotonically increasing function of temperature. A natural gas liquefaction process, called a single mixed refrigeration process, is optimized using the proposed MSHE model under an equation-oriented modeling environment with a physical property package as a case study.
12 July 2018
Optimal synthesis of periodic sorption enhanced reaction processes with application to hydrogen production
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Akhil Arora, Ishan Bajaj, Shachit S. Iyer, M.M. Faruque Hasan A systematic design and synthesis framework for multi-step, multi-mode and periodic sorption-enhanced reaction processes (SERP) is presented. The formulated nonlinear algebraic and partial differential equation (NAPDE)-based model simultaneously identifies optimal SERP cycle configurations, design specifications and operating conditions. Key modeling contributions include a generalized boundary-condition formulation and a representation that enables the selection of discrete operation modes and flow directions using continuous pressure variables. A simulation-based constrained grey-box optimization strategy is employed to obtain optimal cycles and design parameters. The framework has been used for designing two SERP systems, namely sorption-enhanced steam methane reforming (SE-SMR) and sorption-enhanced water gas shift reaction (SE-WGSR), for maximizing hydrogen productivity and minimizing hydrogen-production cost. Specifically, a cyclic SE-SMR process is designed that obtains 95% pure hydrogen from natural gas with 35% higher productivity and 10.86% lower cost compared to existing small-scale, distributed systems. The developed synthesis framework can also be applied for other applications.

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12 July 2018
A CFD simulation study of boiling mechanism and BOG generation in a full-scale LNG storage tank
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Abdullah Saleem, Shamsuzzaman Farooq, Iftekhar A. Karimi, Raja Banerjee Despite heavy insulation, the unavoidable heat leak from the surroundings into an LNG (Liquefied Natural Gas) storage tank causes boil-off-gas (BOG) generation. A comprehensive dynamic CFD simulation of an onshore full-scale LNG tank in a regasification terminal is presented. LNG is approximated as pure methane, the axisymmetric VOF (Volume of Fluid) model is used to track the vapour-liquid interface, and the Lee model is employed to account for the phase change including the effect of static pressure. An extensive investigation of the heat ingress magnitude, internal flow dynamics, and convective heat transfer gives useful insights on the boiling phenomena and a reliable quantification of the BOG. Surface evaporation is the governing boiling mechanism and nucleate boiling is unlikely with proper insulation. The critical wall superheat marking the transition from surface evaporation to nucleate boiling is estimated as 2.5–2.8
8 May 2018
Quality-relevant independent component regression model for virtual sensing application
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Xinmin Zhang, Manabu Kano, Yuan Li Independent component regression (ICR) is an efficient method for tackling non-Gaussian problems. In this work, the defects of the conventional ICR are analyzed, and a novel quality-relevant independent component regression (QR-ICR) method based on distance covariance and distance correlation is proposed. QR-ICR extracts independent components (ICs) using a quality-relevant independent component analysis (QR-ICA) algorithm, which simultaneously maximizes the non-Gaussianity of ICs and statistical dependency between ICs and quality variables. Meanwhile, two new types of statistical criteria, called cumulative percent relevance (CPR) and Max-Dependency (Max-Dep), are proposed to rank the order and determine the number of ICs according to their contributions to quality variables. The proposed QR-ICR(CPR) and QR-ICR(Max-Dep) methods were validated through a vinyl acetate monomer production process and a benchmark near-infrared spectral data. The results have demonstrated that the proposed QR-ICR(CPR) and QR-ICR(Max-Dep) provide simpler predictive models and give better prediction performances than PLS, ICR, ICR(CPR), and ICR(Max-Dep).
8 May 2018
Editorial Board
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113

8 May 2018
Analysis of the dynamics of an active control of the surface potential in metal oxide gas sensors
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Oscar Monge-Villora, Manuel Dominguez-Pumar, Josep M. Olm Gas sensing is nowadays a key actor in pollution observation and detection of chemical toxic agents or explosives. All these applications require the shortest possible time response. Very recently, a control of the surface potential in gas sensors based on metal oxides has experimentally shown to dramatically improve the time response of metal-oxide gas sensors. The proposed control is inspired in sigma-delta modulators. This paper aims at studying the resulting dynamics in the sensor from a theoretical point of view. Using state space models, it is shown how the state variables, namely the concentrations of ionized species in the sensing layer, evolve with time in open and closed loop configuration. This analysis studies how it is possible to alter the dynamics of the overall system, while at the same time keeping some important characteristics of sigma-delta modulators, such as quantization noise-shaping. Numerical simulations validate the obtained results.
8 May 2018
Combining multi-attribute decision-making methods with multi-objective optimization in the design of biomass supply chains
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): J. Wheeler, M.A. P
8 May 2018
An integrated output space partition and optimal control method of multiple-model for nonlinear systems
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Chunyue Song, Bing Wu, Jun Zhao, Zuhua Xu A systematic method for optimally partitioning operating range of each linear subsystem in output space under the criterion of closed-loop performance is initiated, when a multiple model approach is applied to nonlinear systems. As a result, an integrated output space partition and optimal control method is proposed. Firstly, linear input-output models are identified at given operating points and then reformulated as a hybrid model underlying each state having the same physical meaning. Secondly, the optimal state space partition is obtained according to a closed-loop control index. Finally, based on the obtained optimal state space partition, an optimal output space partition is achieved with the projection technique. Furthermore, a hybrid model-MPC strategy is designed according to the obtained multiple-model associated with its optimal output space partition. The integrated output space partition and optimal control method can improve the nonlinear system overall control performance and results of numerical simulation are provided.
8 May 2018
CO2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Bailian Chen, Albert C. Reynolds CO2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO2 injection with the injected water to control the mobility of CO2 and to stabilize the gas front. Optimization of CO2 -WAG injection is widely regarded as a viable technique for controlling the CO2 and oil miscible process. Poor recovery from CO2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However, the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. The proposed framework is demonstrated with three reservoir examples.
8 May 2018
Heuristics with performance guarantees for the minimum number of matches problem in heat recovery network design
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Dimitrios Letsios, Georgia Kouyialis, Ruth Misener Heat exchanger network synthesis exploits excess heat by integrating process hot and cold streams and improves energy efficiency by reducing utility usage. Determining provably good solutions to the minimum number of matches is a bottleneck of designing a heat recovery network using the sequential method. This subproblem is an NP -hard mixed-integer linear program exhibiting combinatorial explosion in the possible hot and cold stream configurations. We explore this challenging optimization problem from a graph theoretic perspective and correlate it with other special optimization problems such as cost flow network and packing problems. In the case of a single temperature interval, we develop a new optimization formulation without problematic big-M parameters. We develop heuristic methods with performance guarantees using three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy packing. Numerical results from a collection of 51 instances substantiate the strength of the methods.
8 May 2018
Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu Iterative optimization technology (IOT), a method that predicts the component composition from only the infrared (IR) spectra of the pure components and mixtures by using Beer's law, has been proposed to reduce the number of calibration samples for process analytical technology in the pharmaceutical industry. However, IOT cannot be applied to mixtures that have wavelength regions where Beer's law does not hold, such as liquid mixtures. The objective of this study is to apply IOT to liquid mixtures to realize a calibration-minimum method. We propose a novel calibration-minimum method that formulates spectral changes by polynomials of the mole fractions considering reasonable boundary conditions for online monitoring. The prediction ability of the proposed method was verified by three case studies: two binary mixtures and one ternary mixture. The model selection strategy, conditions for calibration, and estimation of missing pure component spectra are also discussed. This research represents a step towards advanced calibration-minimum methods.
8 May 2018
Satisfiability modulo theories for process systems engineering
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Miten Mistry, Andrea Callia D’Iddio, Michael Huth, Ruth Misener Process systems engineers have long recognized the importance of both logic and optimization for automated decision-making. But modern challenges in process systems engineering could strongly benefit from methodological contributions in computer science. In particular, we propose satisfiability modulo theories (SMT) for process systems engineering applications. We motivate SMT using a series of test beds and show the applicability of SMT algorithms and implementations on (i) two-dimensional bin packing, (ii) model explainers, and (iii) mixed-integer nonlinear optimization solvers.
8 May 2018
A physiologically-based diffusion-compartment model for transdermal administration – The melatonin case study
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Adriana Savoca, Giovanni Mistraletti, Davide Manca There is a significant hype in the medical sector for the transdermal administration of drugs as it allows achieving a combination of multiple advantages: non-invasive procedure, pain avoidance, no first-pass hepatic metabolism, and induction of sustained plasma levels. This paper proposes a model for the study and prediction of drug transport through skin and the following distribution to human body. This is achieved by an innovative combination of the physiologically-based compartmental approach with Fick's laws of diffusion. The skin model features three strata: stratum corneum, viable epidermis, and dermis, which have a major impact on the absorption, distribution, and metabolism of transdermal drugs. The combined model accounts for skin transport via diffusion equations, and absorption and distribution in the rest of the body (i.e. organs/tissues) via material balances on homogeneous compartments. Experimental data of transdermal melatonin allow validation. Main applications are optimization of the dosage and study of skin transport.

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8 May 2018
Simultaneous identification and optimization of biochemical processes under model-plant mismatch using output uncertainty bounds
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Rubin Hille, Hector M. Budman The method of simultaneous identification and optimization aims at satisfying the conditions of optimality while providing accurate predictions of the process outputs. The model parameters are updated in a run-to-run procedure as to account for changes in operating points and to correct for errors in the predicted gradients of the cost-function and constraints. To make this parameter updating step more robust, we propose a parameter identification objective that includes a ratio of the sum of squared errors to a parametric gradient sensitivity function. This results in an identified set of parameters which provide larger sensitivities for the subsequent gradient correction step thus leading to faster convergence to the optimum. Moreover, worst-case uncertainty bounds on the model outputs are utilized to enforce an adequate model fitting. This is especially valuable when identifying dynamic metabolic models with many parameters. The resulting improvements are illustrated using two simulated cell culture processes.
8 May 2018
A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Lisia S. Dias, Richard C. Pattison, Calvin Tsay, Michael Baldea, Marianthi G. Ierapetritou The integration of dynamic process models in scheduling calculations has recently received significant attention as a mean to improve operational performance in increasingly dynamic markets. In this work, we propose a novel framework for the integration of scheduling and model predictive control (MPC), which is applicable to industrial size problems involving fast changing market conditions. The framework consists on identifying scheduling-relevant process variables, building low-order dynamic models to capture their evolution, and integrating scheduling and MPC by, (i) solving a simulation-optimization problem to define the optimal schedule and, (ii) tracking the schedule in closed-loop using the MPC controller. The efficacy of the framework is demonstrated via a case study that considers an air separation unit operating under real-time electricity pricing. The study shows that significant cost reductions can be achieved with reasonable computational times.
8 May 2018
CFD–Aspen Plus interconnection method. Improving thermodynamic modeling in computational fluid dynamic simulations
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Luis Vaquerizo, Mar
8 May 2018
Large-scale DAE-constrained optimization applied to a modified spouted bed reactor for ethylene production from methane
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): D.M. Yancy-Caballero, L.T. Biegler, R. Guirardello In this paper, a modified spouted bed reactor is proposed to enhance the yield of the oxidative coupling of methane (OCM). Optimization techniques are used to carry out a theoretical analysis of ethylene production via OCM and define some optimal operating conditions of the reacting system. A model-based DAE-constrained optimization strategy is proposed and applied to the OCM process to illustrate the computational capability of the proposed formulation, and the theoretical feasibility of the proposed reactor. The model developed for the reactor is a one-dimensional model composed of material, energy, and momentum balances. This model along with the kinetic model constitute a non-linear and differential-algebraic system, which is discretized using orthogonal collocation on finite elements with continuous profiles approximated by Lagrange polynomials. The resulting algebraic collocation equations are written as equality constraints in the optimization problem, which is solved with the IPOPT solver within the optimization-modeling platform. An initialization routine based on simulations was carried out to guarantee convergence in optimizations. Results from simulations and optimizations showed the potential of combining different reactor concepts to improve the ethylene production from natural gas via oxidative coupling of methane.
8 May 2018
Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Marquis Crose, Weiqi Zhang, Anh Tran, Panagiotis D. Christofides The development of a three-dimensional, multiscale computational fluid dynamics (CFD) model is presented here which aims to capture the deposition of amorphous silicon thin films via plasma-enhanced chemical vapor deposition (PECVD). The macroscopic reactor scale and the microscopic thin film growth domains which define the multiscale model are linked using a dynamic boundary which is updated at the completion of each time step. A novel parallel processing scheme built around a message passing interface (MPI) structure, in conjunction with a distributed collection of kinetic Monte Carlo algorithms, is applied in order to allow for transient simulations to be conducted using a mesh with greater than 1.5 million cells. Due to the frequent issue of thickness non-uniformity in thin film production, an improved PECVD reactor design is proposed. The resulting geometry is shown to reduce the product offset from
8 May 2018
In situ adaptive tabulation (ISAT) for combustion chemistry in a network of perfectly stirred reactors (PSRs) for the investigation of soot formation and growth
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Sudip Adhikari, Alan Sayre, Abhilash J. Chandy This paper presents an efficient computational implementation of the in situ adaptive tabulation (ISAT) approach (Pope, 1997) for combustion chemistry in a network of perfectly stirred reactors (PSRs) for the investigation of soot formation and growth. This study, for the first time, extends the thermochemical composition vector to contain the soot moments, using the method of moments with interpolative closure (MOMIC) as a soot model with six concentration moments. A series of PSR calculations is carried out using the direct integration (DI) and ISAT approaches. Assessment of the accuracy of ISAT approach is conducted through direct comparisons with DI calculations. Moreover, complimentary cumulative distribution function (CCDF), sensitivity of ISAT calculations with respect to the absolute error tolerance values and speedup are analyzed for two different test cases of ethylene–air using two different chemical kinetic mechanisms. A maximum speedup of 50
8 May 2018
Challenges in process optimization for new feedstocks and energy sources
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Alexander Mitsos, Norbert Asprion, Christodoulos A. Floudas, Michael Bortz, Michael Baldea, Dominique Bonvin, Adrian Caspari, Pascal Sch
8 May 2018
Enhancing natural gas-to-liquids (GTL) processes through chemical looping for syngas production: Process synthesis and global optimization
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): William W. Tso, Alexander M. Niziolek, Onur Onel, C. Doga Demirhan, Christodoulos A. Floudas, Efstratios N. Pistikopoulos A process synthesis and global optimization framework is presented to determine the most profitable routes of producing liquid fuels from natural gas through competing technologies. Chemical looping is introduced into the framework for the first time as a natural gas conversion alternative. The underlying phenomena in chemical looping are complex and models from methods such as computational fluid dynamics are unsuitable for global optimization. Therefore, appropriate approximate models are required. Parameter estimation and disjunctive programming are described here for modeling two chemical looping processes. The first is a nickel oxide based process developed at CSIC in Spain; the second is a iron oxide based process developed at Ohio State University. These mathematical models are then incorporated into a comprehensive process superstructure to evaluate the performance of chemical looping against technologies such as autothermal reforming and steam reforming for syngas production. The rest of the superstructure consists of process alternatives for liquid fuels production from syngas and simultaneous heat, power, and water integration. Among the various case studies considered, it is shown that chemical looping can reduce the break-even oil prices for natural gas-to-liquids processes by as much as 40%, while satisfying production demands and obeying environmental constraints. For a natural gas price of $5/TSCF, the break-even price is as low as $32.10/bbl. Sensitivity analysis shows that these prices for chemical looping remain competitive even as natural gas cost rises. The findings suggest that chemical looping is a very promising option to enhance natural gas-to-liquids processes and their capabilities.
8 May 2018
Decoupling the constraints for process simulation in large-scale flowsheet optimization
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Yoshikazu Ishii, Fred D. Otto A distinct advantage of sequential quadratic programming (SQP) is global convergence that ensures convergence from a remote starting point. When the constraints are highly nonlinear such as in flowsheet optimization, however, locally convergent Newton's method used in SQP as the equation-solving tool may deteriorate the behavior of convergence. Our recognition that this issue remains to be resolved motivated us to study a two-tier SQP approach where the constraints for process simulation consisting of nonlinear equations are decoupled from the KKT system in order to block the adverse influence of nonlinearity on global convergence. Our equation oriented (EO) process simulator (Ishii and Otto, 2011) is employed to decouple the constraints and for maintaining feasibility of the decoupled constraints. The effectiveness and potential of the two-tier SQP approach for reliably and efficiently solving large-scale flowsheet optimization problems are numerically illustrated with fully thermally coupled distillation problems.
8 May 2018
Life cycle aggregated sustainability index for the prioritization of industrial systems under data uncertainties
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Jingzheng Ren This study aims at developing a generic method for measuring the sustainability of industrial systems and prioritizing industrial systems under uncertainties. The interval preference relation based goal programming model which can address vagueness and ambiguity existing in human's judgments was employed to determine the weights of the criteria for life cycle sustainability assessment. A life cycle aggregated sustainability index which incorporates both the data of industrial systems with respect to the evaluation criteria and the weights of the criteria was developed to prioritize the industrial systems. An illustrative case including four electricity generation systems were studied by the proposed method, and the results were also validated by another four multi-criteria decision making methods. The results reveal that the developed life cycle aggregated sustainability index can effectively prioritizing industrial systems under data uncertainties.

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8 May 2018
Simulation of hybrid trickle bed reactor–reverse osmosis process for the removal of phenol from wastewater
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): M.A. Al-Obaidi, A.T. Jarullah, C Kara-Za
Available online 22 April 2018
Chromatography Analysis and Design Toolkit (CADET)
Publication date: 8 May 2018
Source:Computers & Chemical Engineering, Volume 113 Author(s): Samuel Leweke, Eric von Lieres CADET is an open source modeling and simulation framework for column liquid chromatography. The software is freely distributed to both academia and industry under the GPL license (http://github.com/modsim/cadet). CADET is based on a core simulator that is written in object oriented C++ and applies modern mathematical algorithms for efficiently solving a variety of customary chromatography models. This simulation engine is interfaced to a suite of MATLAB tools for setting up and executing the most common scientific workflows, e.g., model calibration, process design, robustness analysis, statistical analysis, and experimental design. The model library and numerical methods are continuously extended and improved. For instance, binding models with multiple bound states, pH and/or temperature dependence of binding parameters, surface diffusion, and arbitrary spacing of the radial discretization have been recently added. Moreover, numerical accuracy and computational speed of the code are comprehensively benchmarked using high precision reference solutions and realistic model problems. Versatility of the CADET modeling platform is demonstrated with several examples that are also published as open source code and can be freely adapted to specific use cases. In one of several case studies, sequential and simultaneous optimization of elution gradient shape and cut times are compared for a three component separation. This process is designed to achieve Pareto optimal purity and yield of the central fraction. Moreover, the robustness of these designs with respect to typical process variations is systematically studied. The last case study illustrates the optimal design of experiments for estimating model parameters with maximal accuracy.
Available online 22 April 2018
A novel and systematic approach to identify the design space of pharmaceutical processes
Publication date: Available online 22 April 2018
Source:Computers & Chemical Engineering Author(s): Gabriele Bano, Zilong Wang, Pierantonio Facco, Fabrizio Bezzo, Massimiliano Barolo, Marianthi Ierapetritou Feasibility analysis is a mathematical technique that can be used to assist the identification of the design space (DS) of a pharmaceutical process, given the availability of a process model. One of its main drawbacks is that it suffers from the curse of dimensionality, i.e. simulations can potentially become computationally extremely expensive and very cumbersome when the number of input factors is large. Additionally, giving a graphical and compact representation of the high-dimensional design space is difficult. In this study, we propose a novel and systematic methodology to exploit partial least-squares (PLS) regression modelling to reduce the dimensionality of a feasibility problem. We use PLS to obtain a linear transformation between the original multidimensional input space and a lower dimensional latent space. We then apply a Radial Basis Function (RBF) adaptive sampling feasibility analysis on this lower dimensional space to identify the feasible region of the process. We assess the accuracy and robustness of the results with three metrics, and we critically discuss the criteria that should be adopted for the choice of the number of latent variables. The performance of the methodology is tested on three simulated case studies, one of which involving the continuous direct compaction of a pharmaceutical powder. In all case studies, the methodology shows to be effective in reducing the computational burden while maintaining an accurate and robust identification of the design space.
Available online 20 April 2018
Iterative peptide synthesis in membrane cascades: untangling operational decisions
Publication date: Available online 22 April 2018
Source:Computers & Chemical Engineering Author(s): Wenqian Chen, Mahdi Sharifzadeh, Nilay Shah, Andrew G. Livingston Membrane enhanced peptide synthesis (MEPS) combines liquid-phase synthesis with membrane filtration, avoiding time-consuming separation steps such as precipitation and drying. Although performing MEPS in a multi-stage cascade is advantageous over a single-stage configuration in terms of overall yield, this is offset by the complex combination of operational variables such as the diavolume and recycle ratio in each diafiltration process. This research aims to tackle this problem using dynamic process simulation. The results suggest that the two-stage membrane cascade improves the overall yield of MEPS significantly from 72.2% to 95.3%, although more washing is required to remove impurities as the second-stage membrane retains impurities together with the anchored peptide. This clearly indicates a link between process configuration and operation. While the case study is based on the comparison of single-stage and two-stage MEPS, the results are transferable to other biopolymers such as oligonucleotides, and more complex system configurations (e.g. three-stage MEPS).

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Available online 18 April 2018
Semantically-enabled repositories in multi-disciplinary domains: The case of biorefineries
Publication date: Available online 20 April 2018
Source:Computers & Chemical Engineering Author(s): Eirini Siougkrou, Filopoimin Lykokanellos, Foteini Barla, Antonis C. Kokossis There is an increased use of problem representations (i.e. superstructures in synthesis problems; networks in route problems; graphs; ordered graphs in various systems representations) following on significant advances in optimization technologies that hold capabilities to solve, robustly, large-scale problems. In an attempt to systematically tackle disparate domains and build high-throughput functions, the paper contributes with a semantically-enabled approach systematized and engineered by ontologies. The aim is to develop an intelligent environment with capabilities to build and scale-up system representations, automatically. The work is demonstrated on problems akin to biorenewables and biorefineries; an identical approach is possible to the general problem. Using relations and rules defined among entities, semantics are used to model and expand domains (biorefinery pathways) whereas enabling extracting and creating knowledge. The repository, already on a web-based platform and available as open-source, essentially upgrades conventional representations with capabilities to share (import/export) and integrate its content externally.
Available online 17 April 2018
Multi-parametric Mixed Integer Linear Programming under global uncertainty
Publication date: Available online 18 April 2018
Source:Computers & Chemical Engineering Author(s): Vassilis M. Charitopoulos, Lazaros G. Papageorgiou, Vivek Dua Major application areas of the process systems engineering, such as hybrid control, scheduling and synthesis can be formulated as mixed integer linear programming (MILP) problems and are naturally susceptible to uncertainty. Multi-parametric programming theory forms an active field of research and has proven to provide invaluable tools for decision making under uncertainty. While uncertainty in the right-hand side (RHS) and in the objective function’s coefficients (OFC) have been thoroughly studied in the literature, the case of left-hand side (LHS) uncertainty has attracted significantly less attention mainly because of the computational implications that arise in such a problem. In the present work, we propose a novel algorithm for the analytical solution of multi-parametric MILP (mp-MILP) problems under global uncertainty, i.e. RHS, OFC and LHS. The exact explicit solutions and the corresponding regions of the parametric space are computed while a number of case studies illustrates the merits of the proposed algorithm.
Available online 14 April 2018
Integrated Scheduling and Control in Discrete-time with Dynamic Parameters and Constraints
Publication date: Available online 17 April 2018
Source:Computers & Chemical Engineering Author(s): Logan D.R. Beal, Damon Petersen, David Grimsman, Sean Warnick, John D. Hedengren Integrated scheduling and control (SC) seeks to unify the objectives of the various layers of optimization in manufacturing. This work investigates combining scheduling and control using a nonlinear discrete-time formulation, utilizing the full nonlinear process model throughout the entire horizon. This discrete-time form lends itself to optimization with time-dependent constraints and costs. An approach to combined SC is presented, along with sample pseudo-binary variable functions to ease the computational burden of this approach. An initialization strategy using feedback linearization, nonlinear model predictive control, and continuous-time scheduling optimization is presented. The formulation is applied with a generic continuous stirred tank reactor (CSTR) system in open-loop simulations over a 48-hour horizon and a sample closed-loop implementation. The value of time-based parameters is demonstrated by applying cooling constraints and dynamic energy costs of a sample diurnal cycle, enabling demand response via combined scheduling and control.
Available online 13 April 2018
A Multi-Objective Optimization Approach for Selection of Energy Storage Systems
Publication date: Available online 14 April 2018
Source:Computers & Chemical Engineering Author(s): Lanyu Li, Pei Liu, Zheng Li, Xiaonan Wang Energy storage systems (ESS) are becoming an essential component of energy supply and demand matching. It is important yet complex to find preferable energy storage technologies for a specific application. In this paper, a decision support tool for energy storage selection is proposed; adopting a multi-objective optimization approach based on an augmented
Available online 12 April 2018
Synthesis of Mass Exchange Networks: A Novel Mathematical Programming Approach
Publication date: Available online 13 April 2018
Source:Computers & Chemical Engineering Author(s): Miguel
Available online 11 April 2018
Integrating operations and control: a perspective and roadmap for future research
Publication date: Available online 12 April 2018
Source:Computers & Chemical Engineering Author(s): Prodromos Daoutidis, Jay H. Lee, Iiro Harjunkoski, Sigurd Skogestad, Michael Baldea, Christos Georgakis This “white paper” is a concise perspective based on a session during FIPSE 3, held in Rhodes, Greece, June 20-23, 2016. This was the third conference in the series “Future Innovation in Process Systems Engineering” (http://fi-in-pse.org), which takes place every other year in Greece, with a limited number of participants and just three topics/sessions whose objective is to pose and discuss open research challenges in Process Systems Engineering. This specific session comprised invited talks by Sigurd Skogestad and Iiro Harjunkoski, followed by short presentations by the participants and extensive discussions. The paper does not intend to provide a comprehensive review on the subject, or a detailed exposition of the concepts and problems. Its aim is to highlight open problems and directions for future research.
Available online 10 April 2018
Deep convolutional neural network model based chemical process fault diagnosis
Publication date: Available online 11 April 2018
Source:Computers & Chemical Engineering Author(s): Hao Wu, Jinsong Zhao Numerous accidents in chemical processes have caused emergency shutdowns, property losses, casualties and/or environmental disruptions in the chemical process industry. Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences. However, FDD is still far from widely practical applications. Over the past few years, deep convolutional neural network (DCNN) has shown excellent performance on machine-learning tasks. In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis. The benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance of the fault diagnosis method.
Available online 7 April 2018
A sustainable process design to produce diethyl oxalate considering NOx elimination
Publication date: Available online 10 April 2018
Source:Computers & Chemical Engineering Author(s): Jiaxing Zhu, Lin Hao, Yaozhou S un, Bo Zhang, Wenshuai Bai, Hongyuan Wei Diethyl oxalate (DEO) is widely used in fine chemical industry. In comparison with traditional esterification process, carbon monoxide coupling process is a novel routine for DEO production. This environmentally friendly process provides better selectivity and yield. Its unique feature is that a closed regeneration-coupling circulation is formed. Toxic byproduct-nitric oxide (NO) from coupling reaction is recycled to re-produce ethyl nitrite through regeneration reaction. This avoids significant amount of NOx emission. However, due to a few NOx emission, a contaminant handling system is applied for environmental protection. A systematical environmental analysis is also carried out to assess this process. Regeneration-coupling circulation brings interaction behaviors and some trade-offs including reactor size and recycle flowrate, regeneration and coupling reaction, loss of reactants and NO emission. Thus, a rigorous steady simulation is established to investigate these trade-offs. Then DEO process is optimized to obtain the optimal design. Finally a more economic flowsheet to produce DEO is proposed.
6 April 2018
Optimizing the Design of New and Existing Supply Chains at Dow AgroSciences
Publication date: Available online 7 April 2018
Source:Computers & Chemical Engineering Author(s): Dr. Matt Bassett In this paper, we discuss the design and optimization of supply chains at Dow AgroSciences. We start by introducing the design components of a typical supply chain. We then discuss the typical inputs required in a model and the type of outputs generated. Next we consider the strengths and weaknesses of the standard tools that we use as part of the model process. To show the breadth of problems addressed, three example models are presented. The first problem discusses the complexity of addressing a global supply chain for a new active ingredient. The second problem focused on a regional supply chain. The final problem showed a tactical model looking at rail fleet sizing.
6 April 2018
Editorial Board
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112

6 April 2018
A linearization method for probability moment equations
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Michail Vlysidis, Yiannis N. Kaznessis We present a new method for calculating the time-transient behavior of stochastic reaction networks. We first derive the set of equations for the moments of the master probability distribution. We then linearize these equations calculating the Jacobian matrix around the stationary probability distribution. In order to demonstrate the method, we present examples of stochastic reaction networks and compute their dynamic behavior. We find that the calculations are accurate and significantly more efficient than stochastic simulation algorithms based on Gillespie’s algorithms.

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6 April 2018
Searching historical data segments for process identification in feedback control loops
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Jiandong Wang, Jianjun Su, Yan Zhao, Donghua Zhou Mathematical models of dynamic processes are often required for assessment, diagnosis and improvement of control loop performances in process industries. Data samples collected in daily operations of feedback control loops may enclose data segments suitable for process identification to obtain the mathematical models. This paper proposes a new method to search such data segments. The searching criterion is that the reference and process output in a feedback control loop should experience significant magnitude changes. Hypothesis tests are exploited to find changing positions of data segments with different probability distributions and to verify whether the reference and process output make significant magnitude changes inside one data segment or between two adjacent segments. Simulation and industrial examples are provided to illustrate the effectiveness of the proposed method.
6 April 2018
A Novel Approach for Linearization of a MINLP Stage-Wise Superstructure Formulation
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Anton Beck, Ren
6 April 2018
Effect of cell heterogeneity on isogenic populations with the synthetic genetic toggle switch network: Bifurcation analysis of two-dimensional cell population balance models
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Panagiotis Chrysinas, Michail E. Kavousanakis, Andreas G. Boudouvis The dynamics of gene regulatory networks are often modeled with the assumption of cellular homogeneity. However, this assumption contradicts the plethora of experimental results in a variety of systems, which designates that cell populations are heterogeneous systems in the sense that properties such as size, shape, and DNA/RNA content are unevenly distributed amongst their individuals. In order to address the implications of heterogeneity, we utilize the so-called cell population balance (CPB) models. Here, we solve numerically multivariable CPB models to study the effect of heterogeneity on populations carrying the toggle switch network, which features nonlinear behavior at the single-cell level. In order to answer whether this nonlinear behavior is inherited to the heterogeneous population level, we perform bifurcation analysis on the steady-state solutions of the CPB model. We show that bistability is present at the population level with the pertinent bistability region shrinking when the impact of heterogeneity is enhanced.
6 April 2018
Automated reaction generation for polymer networks
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Yuliia Orlova, Ivan Kryven, Piet D. Iedema Most of the theoretical studies on polymer kinetics has been performed by manually reducing the chemical system to a few simple reaction mechanisms having a repeatable nature. Not being constrained by such reducibility, this work considers the polymerization as a product of a complex network of reactions that need not to be known in advance. Combining various ideas from graph theory, combinatorics and random graphs, we introduce a new modeling approach to complex polymerization that automatically constructs a reaction network, solves kinetic model, and retrieves such topological properties of the final polymer network as, for instance, distribution of molecular weight. In this way, the new approach acts as an intermediate layer that propagates the knowledge of the basic chemistry in order to capture and understand the complexity of the real world polymerizing systems.
6 April 2018
How to tighten a commonly used MINLP superstructure formulation for simultaneous heat exchanger network synthesis
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Anton Beck, Ren
6 April 2018
Multi-level supervision and modification of artificial pancreas control system
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Jianyuan Feng, Iman Hajizadeh, Xia Yu, Mudassir Rashid, Kamuran Turksoy, Sediqeh Samadi, Mert Sevil, Nicole Hobbs, Rachel Brandt, Caterina Lazaro, Zacharie Maloney, Elizabeth Littlejohn, Louis H. Philipson, Ali Cinar Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

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Rigorous synthesis of energy systems by decomposition via time-series aggregation
Publication date: 6 April 2018
Source:Computers & Chemical Engineering, Volume 112 Author(s): Bj
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