Journal Sciences News
Veterinary Clinics of North America: Equine Practice
2 September 2018
Optimal tracking control of artificial gas-lift process
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): Jing Shi, Ahmed Al-Durra, Igor Boiko 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.
2 September 2018
A novel tool for the modeling, simulation and costing of membrane based gas separation processes using Aspen HYSYS: Optimization of the CO2/CH4 separation process
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): Mina Hoorfar, Yousif Alcheikhhamdon, Bo Chen A key tool called “MemCal” is developed to support the simulation of membrane-based gas separation processes using Aspen HYSYS. By integrating “MemCal” with the simulator, users can simulate complex multi-component/multi-stage processes, perform sensitivity studies, and utilize the default HYSYS features to cost and optimize processes. Industrially, “MemCal” can be used to simulate the separation of air, biogas, natural gas, olefins-paraffins and other gases. The CO2/CH4 binary mixture separation was demonstrated in this manuscript as the first and the simplest application of “MemCal”. The simulation of various multi-stage processes suggested that the treatment cost can be dropped by optimizing the separation load distribution among the stages. For CO2/CH4 separation, the simulations suggested that the optimized two-stage process plus recycle resulted in the least separation cost. The installation of a third stage for boosting hydrocarbons recovery was found unjustified when membranes equivalent to or better than cellulose acetate are adopted.
2 September 2018
An optimal control approach to steam distillation of essential oils from aromatic plants
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): F. Valderrama, F. Ruiz In this work, an optimal steam flow trajectory for essential oils extraction from aromatic plants is derived, minimizing energy consumption. A phenomenological dynamic model of the oil extraction process is adopted from literature and a multi-objective optimal control problem is formulated, in order to minimize energy consumption and at the same time maximize the yield of extraction. The resulting optimal control problem is highly non-linear and it is solved by numerical methods. Simulation results are presented for three scenarios: (i) Maximum yield, (ii) minimum energy consumption and (iii) trade-off between yield and energy. It is shown that the optimal steam flow rate trajectory is not necessarily constant. Using a mixed cost-function, it is possible to extract almost 100% of oil essential while saving 60% of energy. Finally, a sensitivity analysis shows that the optimal steam trajectory has few variations when parameters of the plant physiology change.
2 September 2018
Harvest time prediction for batch processes
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): Max Spooner, David Kold, Murat Kulahci Batch processes usually exhibit variation in the time at which individual batches are stopped (referred to as the harvest time). Harvest time is based on the occurrence of some criterion and there may be great uncertainty as to when this criterion will be satisfied. This uncertainty increases the difficulty of scheduling downstream operations and results in fewer completed batches per day. A real case study is presented of a bacteria fermentation process. We consider the problem of predicting the harvest time of a batch in advance to reduce variation and improving batch quality. Lasso regression is used to obtain an interpretable model for predicting the harvest time at an early stage in the batch. A novel method for updating the harvest time predictions as a batch progresses is presented, based on information obtained from online alignment using dynamic time warping.
2 September 2018
A MILP model based on flowrate database for detailed scheduling of a multi-product pipeline with multiple pump stations
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): Qi Liao, Haoran Zhang, Ning Xu, Yongtu Liang, Junao Wang 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.
12 July 2018
A novel MINLP model of front-end crude scheduling for refinery with consideration of inherent upset minimization
Publication date: 2 September 2018
Source:Computers & Chemical Engineering, Volume 117 Author(s): Honglin Qu, Jialin Xu, Sujing Wang, Qiang Xu 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.
12 July 2018
Editorial Board
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115

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
12 July 2018
Optimization-based approach for maximizing profitability of bioethanol supply chain in Brazil
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Andrei Kostin, Diogo H. Macowski, Juliana M.T.A. Pietrobelli, Gonzalo Guill
12 July 2018
A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Bohong Wang, Yongtu Liang, Jianqin Zheng, Tiantian Lei, Meng Yuan, Haoran Zhang One important issue in the mid to late development stages of oilfields is maintaining stable production, especially when the existing gathering pipeline system cannot fully satisfy the development of low pressures and low production rates. In these cases, it is necessary to restructure the original gathering pipeline system. In this study, an optimal design method is proposed to restructure a pipeline system in an oilfield in the mid to late development stages. Based on the demand of stable production and the existing structure of the pipeline system, a mixed-integer nonlinear programming (MINLP) model with an objective function that minimizes the total cost is developed. Hydraulic, technical and economic constraints are considered. The model is linearized based on a piecewise method and solved by the branch-and-bound algorithm. This method is applied to a real case study of a pipeline system in an oilfield.
12 July 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).
12 July 2018
Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Mahsa Sadeghassadi, Chris J.B. Macnab, Bhushan Gopaluni, David Westwick This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the influent changes are periodic and thus predictable. Specifically, a nonlinear optimization procedure utilizes dry weather data to decide on a nominal fixed setpoint, or a weighting gain, or both; during weather events an algorithm uses the optimization solution(s) together with the ammonium predictions to adjust the setpoint dynamically (responding appropriately to significant changes in the influent). A constrained nonlinear neural-network model predictive control tracks the setpoint. Simulations with the BSM1 compare several variations of the proposed methods to a fixed-setpoint PI control, demonstrating improvement in effluent quality or reduction in energy use, or both.
12 July 2018
Optimization of dimethyl ether production process based on sustainability criteria using a homotopy continuation method
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Javad Asadi, Farhang Jalali Farahani Traditional criteria for designing processes that utilize only economic aspects can have a negative impact on the environment and society. In this study, a process for the production of dimethyl ether (DME) from methanol is evaluated by employing sustainability metrics. Operational conditions are optimized by implementing a rigorous global multi-objective optimization algorithm based on maximization of economic performance measured by the return on investment (ROI) and minimization of environmental and social impacts. The most efficient operational conditions for DME production based on sustainability criteria are obtained with a homotopy continuation method in conjunction with a process simulator. The resulting conditions indicate that the global warming metric of the DME process is decreased more than 10 times and the decrease of photochemical smog formation, mass intensity and energy intensity is 96%, 12% and 9%, respectively. The optimized, sustainable process shows an only an insignificant reduction in terms of economic aspect.
12 July 2018
Integrating operations and control: A perspective and roadmap for future research
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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.
12 July 2018
Deep convolutional neural network model based chemical process fault diagnosis
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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.
12 July 2018
A sustainable process design to produce diethyl oxalate considering NOx elimination
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Jiaxing Zhu, Lin Hao, Yaozhou Sun, 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.
12 July 2018
A multi-objective optimization approach for selection of energy storage systems
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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
12 July 2018
Synthesis of mass exchange networks: A novel mathematical programming approach
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Miguel
12 July 2018
A decision support platform for a bio-based supply chain: Application to the region of Lower Saxony and Bremen (Germany).
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Christos Galanopoulos, Diego Barletta, Edwin Zondervan In this work, a biomass supply chain model, for the region of Lower Saxony and Bremen in northern Germany, has been developed. Because of Germany's high demand for biofuels, the production and distribution of levulinic acid and bioethanol by wheat straw is studied. An illustrative bio-based supply chain model is developed and implemented in the Advanced Interactive Multidimensional Modeling (AIMMS) software. Then, this model is used to study the logistics, network optimization, transportation and inventory management, and the resulting environmental and economic impacts. In the end, a sensitivity analysis is conducted to evaluate the influence of key model parameters on these impacts. The results showed that a wheat straw supply chain network is profitable in the area of Bremen and Lower Saxony even though the bioproducts demand is not fully covered and that the transportation costs did not have a strong impact on the supply chain network.
12 July 2018
Errata: Heat exchanger network cleaning scheduling: From optimal control to mixed-integer decision making
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Sai Darshan Adloor, Riham Al Ismaili, D. Ian Wilson, Vassilios S. Vassiliadis Errata to the article by Al Ismaili et al. (2018) on the optimal scheduling of cleaning actions for Heat Exchanger Networks subject to fouling are presented. Errors present in the equations of the Pontryagin Minimum Principle analysis of the original article are indicated and rectified. It is noted that despite these errors, there is no change to the conclusions of the analysis given in Al Ismaili et al. (2018).
12 July 2018
Active directional modifier adaptation for real-time optimization
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): M. Singhal, A.G. Marchetti, T. Faulwasser, D. Bonvin Modifier adaptation is a real-time optimization (RTO) methodology that uses plant gradient estimates to correct model gradients, thereby driving the plant to optimality. However, obtaining accurate gradient estimates requires costly plant experiments at each RTO iteration. In directional modifier adaptation (DMA), the model gradients are corrected only in a small subspace of the input space, thus requiring fewer plant experiments. DMA selects the input subspace offline based on the local sensitivity of the Lagrangian gradient with respect to the uncertain model parameters. Here, we propose an extension, whereby the input subspace is selected at each RTO iteration via global sensitivity analysis, thus making the approach more reactive to changes and robust to large parametric uncertainties. Simulation results performed on the run-to-run optimization of two different semi-batch reactors show that the proposed approach finds a nice balance between experimental cost and optimality.
12 July 2018
NARX modeling for real-time optimization of air and gas compression systems in chemical processes
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Won Je Lee, Jonggeol Na, Kyeongsu Kim, Chul-Jin Lee, Younggeun Lee, Jong Min Lee This study considers the Nonlinear Autoregressive eXogenous Neural Net model (NARX NN) based real-time optimization (RTO) for industrial-scale air & gas compression system in a commercial terephthalic acid manufacturing plant. NARX model is constructed to consider time-dependent system characteristics using actual plant operation data. The prediction performance is improved by extracting the thermodynamic characteristics of the chemical process as a feature of this model. And a systematic RTO method is suggested for calculating an optimal operating condition of compression system by recursively updating the NARX model. The performance of the proposed NARX model and RTO methodology is exemplified with a virtual plant that simulates the onsite commercial plant with 99.6% accuracy. NARX with feature extraction model reduces mean squared prediction error with the actual plant data 43.5% compared to that of the simple feed-forward multi-perceptron neural networks. The proposed RTO method suggests optimal operating conditions that reduce power consumption 4%.
12 July 2018
Iterative peptide synthesis in membrane cascades: Untangling operational decisions
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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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12 July 2018
Random Forests for mapping and analysis of microkinetics models
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Behnam Partopour, Randy C. Paffenroth, Anthony G. Dixon We introduce the application of an ensemble learning method known as Random Forests to microkinetics modeling and the computationally efficient integration of microkinetics into reaction engineering models. First, we show how Random Forests can be used for mapping pre-computed microkinetics data. Random Forests can be used to predict new datasets while keeping the prediction accuracy high and the computational load low. The method is also used to identify the important variables in the mechanism in regard to overall reaction rate and selectivity. The results are compared with results from a similar study using the Campbell's Degree of Rate Control approach and it is shown that the Random Forests method could be used to identify important features of the mechanism over a wide range of reacting conditions. Finally, the inclusion of the suggested method into reaction engineering models such as Computational Fluid Dynamics (CFD) resolved-particle simulations of fixed bed reactors is presented.
12 July 2018
A machine learning based computer-aided molecular design/screening methodology for fragrance molecules
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Lei Zhang, Haitao Mao, Linlin Liu, Jian Du, Rafiqul Gani Although the business of flavors and fragrances has become a multibillion dollar market, the design/screening of fragrances still relies on the experience of specialists as well as available odor databases. Potentially better products, however, could be missed when employing this approach. Therefore, a computer-aided molecular design/screening method is developed in this work for the design and screening of fragrance molecules as an important first step. In this method, the odor of the molecules are predicted using a data driven machine learning approach, while a group contribution based method is employed for prediction of important physical properties, such as, vapor pressure, solubility parameter and viscosity. A MILP/MINLP model is established for the design and screening of fragrance molecules. Decomposition-based solution approach is used to obtain the optimal result. Finally, case studies are presented to highlight the application of the proposed fragrance design/screening method.
12 July 2018
A novel and systematic approach to identify the design space of pharmaceutical processes
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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.
12 July 2018
MILP models for objective reduction in multi-objective optimization: Error measurement considerations and non-redundancy ratio
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Daniel V
12 July 2018
A real-time optimization framework for the time-varying economic environment
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Qun Wu, Yugeng Xi, Zoltan Nagy, Dewei Li In this paper, we propose a conceptual framework for nonlinear systems to integrate real-time optimization (RTO) and model predictive control (MPC) under time-varying economic environments. In the RTO layer, we introduce a lookup table including a large number of steady-state points of the nonlinear system, which are predetermined offline. Once the parameters of the economic cost function are varied, we are able to take a quick online search on the lookup table to find a point for satisfied economic performance and then send it to the MPC layer as a temporary control target. The temporary target is also employed as the initial solution for solving the optimization of the RTO layer. When the optimal target is calculated by RTO, it will replace the temporary one as the new control target of MPC. Compared to the two-layer framework which suffers from long waiting time to get the optimal operating points, the lookup-table-based RTO (LT-RTO) framework provides a quick-produced suboptimal target for MPC. It avoids unnecessary economic losses if MPC is still tracking outdated target even parameters of the cost function have already changed. We demonstrate the effectiveness through a chemical process model that the LT-RTO framework makes an improvement of the economic performance.
12 July 2018
Data-driven rolling-horizon robust optimization for petrochemical scheduling using probability density contours
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Yi Zhang, Yiping Feng, Gang Rong In the process industry, uncertain factors, such as yield, can be quantified by analyzing industrial data generated from continuous sources. Traditional data-driven robust optimization models are mostly built on estimated probability distributions and convex uncertainty sets. As a result, the scheduling solution is only applicable to the limited sample of stochastic scenarios. We developed a rolling-horizon optimization approach to adapt the robust model to the changing environmental and operational conditions. First, a novel uncertainty set is defined by the probability density contours, covering scenarios with high possibility of occurrence. Then, we propose using new robust formulations induced by the outer-approximations of nonconvex uncertainty set. By implementing the raised model on a real-world ethylene production process using the available data, the fluctuation in fuel gas consumption can be controlled within 2%. Additionally, in agreement with our proof, the system’s total profit and consumption of fuel gas stabilize in finite steps.
12 July 2018
Integrated scheduling and control in discrete-time with dynamic parameters and constraints
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 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-h 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.
12 July 2018
Multiproduct pipeline scheduling integrating for inbound and outbound inventory management
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Diovanina Dimas, Val
12 July 2018
Nonlinear dynamic analysis and control design of a solvent-based post-combustion CO2 capture process
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Xiao Wu, Jiong Shen, Yiguo Li, Meihong Wang, Adekola Lawal, Kwang Y. Lee A flexible operation of the solvent-based post-combustion CO2 capture (PCC) process is of great importance to make the technology widely used in the power industry. However, in case of a wide range of operation, the presence of process nonlinearity may degrade the performance of the pre-designed linear controller. This paper gives a comprehensive analysis of the dynamic behavior and nonlinearity distribution of the PCC process. Three cases are taken into account during the investigation: 1) capture rate change; 2) flue gas flowrate change; and 3) re-boiler temperature change. The investigations show that the CO2 capture process does have strong nonlinearity; however, by selecting a suitable control target and operating range, a single linear controller is possible to control the capture system within this range. Based on the analysis results, a linear model predictive controller is designed for the CO2 capture process. Simulations of the designed controller on an MEA based PCC plant demonstrate the effectiveness of the proposed control approach.

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12 July 2018
Comparison of flowsheets for THF/water separation using pressure-swing distillation
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): William L. Luyben An eloquent optimization method was recently proposed and tested on the separation of tetrahydrofuran and water using pressure-swing distillation. The purpose of this paper is to compare the results of this rigorous optimum design with the design presented in a 1985 paper, which used heuristic engineering optimization. The new design uses more feed preheating in the low-pressure column and a higher pressure with more trays in the high-pressure column. Simulation results show that the old design has a 14% lower energy cost.
12 July 2018
Linguistic OWA and two time-windows based fault identification in wide plants
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): A. S
12 July 2018
Efficient sampling algorithm for large-scale optimization under uncertainty problems
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Nishant Dige, Urmila Diwekar Uncertainty is part of the real-world optimization problems. The major bottleneck in solving large-scale stochastic optimization problems is the computational intensity of scenarios or samples. To this end, this research presents a novel sampling approach. This sampling called LHS-SOBOL combines one-dimensional uniformity of LHS and d-dimensional uniformity of Sobol. This paper analyzes existing and novel sampling techniques by conducting large-scale experiments with different functions. The sampling techniques which are analyzed are Monte Carlo Sampling (MCS), Latin Hypercube Sampling (LHS), Hammersley Sequence Sampling (HSS), Latin Hypercube-Hammersley Sequence Sampling (LHS-HSS), Sobol Sampling, and the proposed novel Latin Hypercube-Sobol Sampling (LHS-SOBOL). It was found that HSS performs better up to 40 uncertain variables, Sobol up to 100 variables, LHS-HSS up to 250 variables, and LHS-SOBOL for large-scale uncertainties for larger than 100 variables.
12 July 2018
Security constrained unit commitment scheduling: A new MILP formulation for solving transmission constraints
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Gonzalo E. Alvarez, Marian G. Marcovecchio, P
12 July 2018
A distributed feasible-side convergent modifier-adaptation scheme for interconnected systems, with application to gas-compressor stations
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Predrag Milosavljevic, Ren
12 July 2018
Two methods of data reconciliation for pipeline networks
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Joshua D. Isom, Andrew T. Stamps, Ali Esmaili, Camilo Mancilla The paper compares the two most common methods of data reconciliation for pipeline networks. The first method, an unscented Kalman filter (UKF), uses a system of nonlinear implicit ordinary differential equations derived from the governing partial differential equations. The second method, a quadratic program, relies on a transformation of the system of nonlinear ordinary differential equations into a set of linear difference equations, with the linearization optimized for known pressure and flow ranges using a novel linearization technique. Both the UKF and the quadratic programming approaches for data reconciliation in gas pipeline networks are viable for networks of small to moderate size. Given the reduced number of simplifying assumptions and the resulting improved accuracy, the UKF may be preferable when the computational problem is tractable. The quadratic programming approach is faster, accepts lower fidelity models, and provides acceptable accuracy.
Available online 22 June 2018
Feature extraction and reduced-order modelling of nitrogen plasma models using principal component analysis
Publication date: 12 July 2018
Source:Computers & Chemical Engineering, Volume 115 Author(s): Aur
Available online 20 June 2018
Carbon dioxide adsorption separation from dry and humid CO2/N2 mixture
Publication date: Available online 22 June 2018
Source:Computers & Chemical Engineering Author(s): Rached Ben-Mansour, Naef A.A. Qasem, Mohammed A. Antar In this study, we report the effect of water vapor on CO2 uptake using Mg-MOF-74 via adsorption breakthrough modeling and lab experiments. Carbon dioxide is the most influencing gas that significantly expedites global warming. Therefore, it is ultimately necessary to reduce the rapid increase of CO2 concentration in the atmosphere by means of Carbon Capture and Storage (CCS). CO2 separation by physical adsorption is an interesting technology to achieve CO2 capture with minimum energy penalties. Metal-organic framework (MOF) adsorbents forms a class of adsorbents with much higher specific surface areas than conventional porous materials such as activated carbons, and zeolites. However, most MOFs show notable hydro instability for CO2 separation from humid flue gas. Mg-MOF-74 is a superior adsorbent amongst other adsorbents owing to its high CO2 uptake at flue gas conditions. A model is developed using User-Defined-Function in an ANSYS Fluent program. Two and three-dimensional models are validated by comparing their results with experimental work carried out by the authors, at ambient temperature, and published experimental data for high temperature conditions. The effect of water vapor is studied at different temperatures and various relative humidity values for Mg-MOF-74. Results indicate that CO2 uptake has been significantly reduced with the existence of more than 5% water vapor when Mg-MOF-74 is used as an adsorbent.
Available online 15 June 2018
On the computation and physical interpretation of semi-positive reaction network invariants
Publication date: Available online 20 June 2018
Source:Computers & Chemical Engineering Author(s): Aisha Alobaid, Hossein Salami, Raymond A. Adomaitis 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.
Available online 15 June 2018
Multi-objective Optimization of an Integrated Gasification Combined Cycle for Hydrogen and Electricity Production
Publication date: Available online 15 June 2018
Source:Computers & Chemical Engineering Author(s): Maan Al-Zareer, Ibrahim Dincer, Marc A. Rosen 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 neural network 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.

Multiobjective decision-support tools for the choice between single-use and multi-use technologies in sterile filling of biopharmaceuticals
Publication date: Available online 15 June 2018
Source:Computers & Chemical Engineering Author(s): Haruku Shirahata, Masahiko Hirao, Hirokazu Sugiyama In sterile filling of biopharmaceuticals, two equipment technologies are available, namely, a conventional multi-use technology using stainless steel fixed facilities, and a new single-use technology using resin-made disposable equipment. For the choice between these technologies, this study proposes a set of three multiobjective decisionsupport tools. The first tool is to evaluate cost, environmental impact, product quality, and supply robustness; the second uses a set ofweighting factors to produce a total score; the third conducts a sensitivity analysis to investigate the influence of the weighting factors on the final decision. The use of these tools was described as an activity model by a method called "the type zero method of integration definition for function modeling" (IDEF0). A case study was conducted to demonstrate the tools and the activity model in different production patterns, i.e., from small-scale and multiproduct to large-scale and single-product.
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