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  • A multi-level programming for shale gas-water supply chains accounting for tradeoffs between economic and environmental concerns
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-25
    Yizhong Chen; Xi Cheng; Jing Li; Li He

    This study develops a multi-level programming model for the planning shale gas-water supply chains. A set of leader-follower-interactive objectives with emphases of water consumption, economic performance, and pollutant discharge are integrated into a sequential decision-making process. Satisfactory degree is introduced to tackle the computationally challenging problem based on an interactive fuzzy approach. Operational decisions regarding water resources allocation, transportation mode selection, and pollutant discharge control would be achieved. Results reveal that the environmentally- or economically-aggressive strategies would be generated when a single goal for optimizing economic benefits or pollutant discharge. The multi-objective decisions are limited by the selected weights. However, the multi-level model would provide more comprehensive schemes due to its sequential consideration of the economic/environmental concerns. Findings from the multi-level model can facilitate (a) balancing the conflicts among different decision makers, (b) timing and siting for shale gas production, and (c) managing water resources for pollutant discharge control.

    更新日期:2020-01-26
  • Regression and independence based variable importance measure
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-25
    Xinmin Zhang; Takuya Wada; Koichi Fujiwara; Manabu Kano

    Evaluating the importance of input (predictor) variables is of interest in many applications of statistical models. However, nonlinearity and correlation among variables make it difficult to measure variable importance accurately. In this work, a novel variable importance measure, called regression and independence based variable importance (RIVI), is proposed. RIVI is designed by integrating Gaussian process regression (GPR) and Hilbert-Schmidt independence criterion (HSIC) so that it is applicable to nonlinear systems. The results of two numerical examples demonstrate that RIVI is superior to several conventional measures including the Pearson correlation coefficient, PLS-β, PLS-VIP, Lasso, HSIC, and permutation importance with random forest in the variable identification accuracy.

    更新日期:2020-01-26
  • In silico study of the microalgae−bacteria symbiotic system in a stagnant pond
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Maritza E. Cervantes-Gaxiola; Oscar M. Hernández-Calderón; Eusiel Rubio-Castro; Jesús R. Ortiz-del-Castillo; Marcos D. González-Llanes; Erika Y. Rios-Iribe

    A rigorous mathematical modeling of the symbiotic interaction between microalgae and bacteria in a stagnant pond is applied to analyze the effect of the operating conditions on the bioprocess kinetics. The microbial co-culture is described by a partial differential equations system, which is solved by a combined numerical method based on the Lagrangian Particle Tracking for microalgae transport equation and the Orthogonal Cubic Hermite Collocation for remaining transport equations. The effect of the temperature and light intensity, alkalinity, turbidity, initial ratio of biomasses, algal cell size, pond depth on the algal biomass productivity and the substrates removal is analyzed. It was found that all of them significantly affect the biomass production and the substrates removal, which is discussed in detail. Besides, a strong symbiotic interaction between cell growth of microalgae and bacteria is observed; specifically, bacterial growth was restricted by the microalgal growth, due to the limitation of dissolved oxygen.

    更新日期:2020-01-24
  • An accelerated dual method based on analytical extrapolation for distributed quadratic optimization of large-scale production complexes
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Lukas Samuel Maxeiner; Sebastian Engell

    Chemical production sites usually consist of plants that are owned by different companies or business units but are tightly connected by streams of materials and carriers of energy. Distributed optimization, where each entity optimizes its objective and the transfer prices of energy and materials are adapted by a coordinator, is a promising approach to this kind of problems, as confidentiality of internal data can be preserved. In this contribution, we propose an extension of the widely used subgradient methods for inequality constrained distributed QPs, which we call analytical extrapolation (AE). Therein, the analytical structure of the dual function is exploited to speed up convergence. Two strategies for handling changing sets of active constraints are presented. We investigate the performance of our algorithm on test problems, where different problem parameters are varied, and show that the performance of our algorithm is in most cases significantly better than that of other methods.

    更新日期:2020-01-24
  • Adaptive predictive control of bioprocesses with constraint-based modeling and estimation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Banafsheh Jabarivelisdeh; Lisa Carius; Rolf Findeisen; Steffen Waldherr

    Control of biotechnological processes is currently recipe-based with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, model-based optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of model-based control, it is crucial to use flexible and adaptive control strategies which address biological variability while compensating for uncertainties. In this work, we present an approach for adaptive control of a bioprocess based on dynamic flux balance models. A previously developed bilevel approach for bioprocess optimization is implemented inside a model predictive control (MPC) routine. To account for model uncertainties, a moving horizon estimation algorithm is combined with the MPC in order to estimate uncertain parameters of the underlying model online for different metabolic modes. We apply this method to maximize the productivity of a target metabolite under microaerobic conditions by adapting the degree of oxygen-limitation online.

    更新日期:2020-01-24
  • Nonlinear Model Predictive Control of an Industrial Process with Steady-state Gain Inversion
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Rahul Bindlish

    Nonlinear model predictive controller (NMPC) is formulated for an industrial process with steady-state gain inversion so that the output variable can be maximized at the peak in presence of disturbances. Appropriate disturbance model formulation is used along with a novel output measurement to allow for robust control in presence of both measured and unmeasured disturbances at the peak where steady-state gain inversion occurs. The constrained nonlinear controller for a process with steady-state gain inversion has been applied successfully with results from plant data that show robustness in maximizing the selectivity of effluent ethylene oxide in the industrial ethylene epoxidation reactor.

    更新日期:2020-01-24
  • Biomass-Based Integrated Gasification Combined Cycle With Post-Combustion Co2 Recovery by Potassium Carbonate: Techno-Economic and Environmental Analysis
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Ikhlas Ghiat; Ahmed AlNouss; Gordon McKay; Tareq Al-Ansari

    In this study, a thermodynamic model depicting integrated bioenergy with carbon capture and storage (BECCS) system is developed using Aspen Plus under thermodynamic equilibrium for the power generation segment, and a rate-based model for the carbon capture segment representing CO2 recovery from the exhaust flue of a biomass based integrated gasification combined cycle (BIGCC). A thorough techno-economic analysis is conducted for the integrated system to evaluate system-wide environmental impacts and economic costs. The carbon capture is modelled using post combustion technology with chemical absorption by means of Piperazine promoted potassium carbonate to absorb the CO2 from the exhaust stream of the gas turbine. The results demonstrate that the proposed system with 80% carbon capture has negative emissions of -0.31 kg/kWh of CO2-e, when assuming neutral emissions from the BIGCC. For a production of 419 kW of net electricity, the overall energy and exergy efficiencies are 43.8% and 57.2% respectively.

    更新日期:2020-01-24
  • An artificial neural network approach to recognise kinetic models from experimental data
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-24
    Marco Quaglio; Louise Roberts; Mohd Safarizal Bin Jaapar; Eric S. Fraga; Vivek Dua; Federico Galvanin

    The quantitative description of the dynamic behaviour of reacting systems requires the identification of an appropriate set of kinetic model equations. The selection of the correct model may pose substantial challenges as there may be a large number of candidate kinetic model structures. In this work, a model selection approach is presented where an Artificial Neural Network classifier is trained for recognising appropriate kinetic model structures given the available experimental evidence. The method does not require the fitting of kinetic parameters and it is well suited when there is a high number of candidate kinetic mechanisms. The approach is demonstrated on a simulated case study on the selection of a kinetic model for describing the dynamics of a three-component reacting system in a batch reactor. The sensitivity of the approach to a change in the experimental design and to a change in the system noise is assessed.

    更新日期:2020-01-24
  • A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    Shaodong Zheng; Jinsong Zhao

    Process monitoring plays an important role in chemical process safety management, and fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches, supervised ones are inappropriate for industrial applications due to the lack of labeled historical data in real situations. Thereby, unsupervised methods which are capable of dealing with unlabeled data should be developed for fault diagnosis. In this work, a new unsupervised data mining method based on deep learning is proposed for isolating different conditions of chemical process, including normal operations and faults, and thus labeled database can be created efficiently for constructing fault diagnosis model. The proposed method mainly consists of three steps: feature extraction by the convolutional stacked autoencoder (SAE), feature visualization by the t-distributed stochastic neighbor embedding (t-SNE) algorithm, and clustering. The benchmark Tennessee Eastman process (TEP) and an industrial hydrocracking instance are utilized to illustrate the effectiveness of the proposed data mining method.

    更新日期:2020-01-23
  • Optimisation of multi effect distillation based desalination system for minimum production cost for freshwater via repetitive simulation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    O.M.A. Al-hotmani; M.A. Al-Obaidi; G. Filippini; F. Manenti; R. Patel; I.M. Mujtaba

    The shortage of fresh water resources is a global problem which requires a prompt solution. Thus, the multi effect distillation (MED) was successfully used for the production of fresh water from seawater. Despite the use of MED desalination system extensively, the influence of the number of effects on the fresh water production cost has not been covered in the open literature. Thus, this paper tries to rectify this specific challenge via simulation at given operating conditions of seawater salinity and temperature. The study is performed using a detailed mathematical model contains the suitable cost correlations. gPROMS model builder suite has been used to carry out an extensive simulation. The results of the study show that the lowest fresh water production cost can be achieved at an optimal number of effects of 17 for a certain operating conditions.

    更新日期:2020-01-23
  • Novel MINLP Formulations for Flexibility Analysis for Measured and Unmeasured Uncertain Parameters
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    M. Paz Ochoa; Ignacio E. Grossmann

    In this work, we formulate the extended flexibility analysis, which takes into account two different types of uncertain parameters: measured (θm) and unmeasured (θu), as a rigorous multi-level optimization problem. We recursively reformulate the inner optimization problems by the KKT conditions and with a mixed-integer representation of the complementarity conditions to solve the resulting multilevel optimization problem. Special cases are identified, where models are comprised of convex constraints or constraints with monotonic variation of the uncertain parameters. In these cases, a vertex enumeration can be performed to solve the flexibility test. We propose two MINLP reformulations for the more general case yielding to similar results but different model sizes. The formulations are tested and compared with several examples.

    更新日期:2020-01-23
  • Dynamic modeling and experimental validation of essential oils fractionation: application for the production of phenylpropanoids
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    Julián Arias; Daniel Casas-Orozco; Andrés Cáceres-León; Jairo Martí-nez; Elena Stashenko; Aída-Luz Villa

    Batch distillation is useful in the essential oil (EO) industry to standardize and improve EO properties. Using the Lippia origanoides EO as a source of phenylpropanoids, a methodology was developed to solve and experimentally validate a batch distillation model, which described separation of EO major constituents over time. Nine EOs were distilled and their composition and distillation products were determined. Seven major constituents were used to represent the EOs and their distillation products in the mathematical analyses performed, namely, data reconciliation to modify the streams compositions in order to meet material balances, and a rigorous distillation model to describe the system dynamics. Statistical parameters (r2=0.95, MSE=0.002) were calculated to compare predicted and experimental data, showing that the model can accurately predict the composition of distillation cuts. This methodology can be extended to other EOs of industrial interest to support their fractionation processes.

    更新日期:2020-01-23
  • Open loop testing for optimizing the closed loop operation of chemical systems
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    M. Dominguez-Pumar; J.M. Olm; L. Kowalski; V. Jimenez

    One way to optimize the operation of chemical systems consists on using closed loop controls while the system is operated. This way, it is possible to optimize the operating point in the plant, process or chemical system so that certain quantities are maximized/minimized or time responses are reduced. In the design of the closed loop controls, though, one of the problems is the large number of configuration parameters involved and the uncertainties in the system. The objective of this paper is to present how recently proposed controls, exploiting time-scale separation, allow to configure their closed loop configuration parameters from open-loop measurements. These controls have been proposed to accelerate the time response of metal-oxide gas sensors by operating at constant surface potential. The main result of the paper explains the link between the open loop measurements and closed loop operation. Experiments are provided using a commercial off-the-shelf Microelectromechanical gas sensor.

    更新日期:2020-01-23
  • Feasibility Study on Replacement of Atmospheric Distillation Column with New Sequences in a Natural Gas Condensate Refinery
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    Amirhossein Khalili-Garakani; Norollah Kasiri; Javad Ivakpour
    更新日期:2020-01-23
  • A Lagrangean Decomposition Optimization Approach for Long-Term Planning, Scheduling and Control
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-23
    Dante Mora-Mariano; Miguel Angel Gutiérrez-Limón; Antonio Flores-Tlacuahuac

    A decomposition strategy to address the simultaneous solution of large-scale planning, scheduling and control problems (PSC) is proposed in this work. Long-term PSC problems are hard to solve because of the large number of both discrete and continuous decision variables embedded in such optimization formulations. Improved optimal decisions can be realized by taking into account natural interactions present in PSC problems. This is the main justification for a simultaneous solution approach of such optimization problems, although this consideration strongly increases the computational solution of PSC problems. In this work, the integrated PSC problem is reformulated using Lagrangean Decomposition, resulting a model decomposed into planning and scheduling (PS) and control (C) subproblems. In the case study, the proposed solution strategy was applied to a multiproduct CSTR represented by the model of Hicks and Ray, which presents strong nonlinear behaviour, over planning horizons of three, four, eight, twelve, and sixteen periods lasting one week each. Moreover, the PSC model incorporates a non-linear model predictive control (NMPC) scheme in order to realize dynamic transitions which are as smooth as possible. The results were compared, in terms of the optimal profit and the CPU time consumed, against those produced by the direct solution of the problem (without using a decomposition strategy) for the specific case of three planning periods, showing a significant reduction in the computational effort.

    更新日期:2020-01-23
  • An Optimal Control Approach to Scheduling and Production in a Process using Decaying Catalysts
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-20
    S.D. Adloor; T. Pons; V.S. Vassiliadis

    This article presents a novel approach to optimise scheduling and production planning to meet seasonal demand in an industrial process using decaying catalysts, based on its formulation as a multistage mixed-integer optimal control problem (MSMIOCP). Unlike existing methodologies, the MSMIOCP formulation allows to solve this problem as a standard nonlinear optimisation problem without combinatorial optimisation methods, which can be advantageous in providing reliable, robust and efficient solutions. Using this formulation, four case studies of this problem, differing in reaction or deactivation kinetics, are investigated. Two different solution implementations are used, each having their own relative advantages. The first implementation demonstrates a bang-bang behaviour for the linear scheduling controls, consistent with a theoretical analysis, but faces integration problems and does not always produce high quality solutions. The second implementation, while not demonstrating the bang-bang property, always produces high quality solutions and shows the advantages of the MSMIOCP formulation over existing methodologies.

    更新日期:2020-01-21
  • Analytical time-stepping solution of the discretized population balance equation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-17
    Mohamed Ali Jama; Wenli Zhao; Waqar Ahmed; Antonio Buffo; Ville Alopaeus

    The prediction of the particle-size distribution (PSD) of the particulate systems in chemical engineering is very important in a variety of different contexts, such as parameter identification, troubleshooting, process control, design, product quality, production economics etc. The time evolution of the PSD can be evaluated by means of the population balance equation (PBE), which is a complex integro-differential equation, whose solution in practical cases always requires sophisticated numerical methods that may be computationally tedious. In this work, we propose a novel technique that tackles this issue by using an analytical time-stepping procedure (ATS) to resolve the PSD time dependency. The ATS is an explicit time integrator, taking advantage of the linear or almost linear time dependency of the discretized population balance equation. Thus, linear approximation of the source term is a precondition for the ATS simulations. The presented technique is compared with a standard variable step time integrator (MATLAB ODE15s stiff solver), for practical examples e.g. emulsion, ageing cellulose process, cooling crystallization, reactive dissolution, and liquid-liquid extraction. The results show that this advancing in time procedure is accurate for all tested practical examples, allowing reproducing the same results given by standard time integrators in a fraction of the computational time.

    更新日期:2020-01-17
  • Benchmark Temperature Microcontroller for Process Dynamics and Control
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-15
    Junho Park; R. Abraham Martin; Jeffrey D. Kelly; John D. Hedengren

    Standard benchmarks are important repositories to establish comparisons between competing model and control methods, especially when a new method is proposed. This paper presents details of an Arduino micro-controller temperature control lab as a benchmark for modeling and control methods. As opposed to simulation studies, a physical benchmark considers real process characteristics such as the requirement to meet a cycle time, discrete sampling intervals, communication overhead with the process, and model mismatch. An example case study of the benchmark is quantifying an optimization approach for a PID controller with 5.4% improved performance. A multivariate example shows the quantified performance improvement by using model predictive control with a physics-based model, an autoregressive time series model, and a Hammerstein model with an artificial neural network to capture the static nonlinearity. These results demonstrate the potential of a hardware benchmark for transient modeling and regulatory or advanced control methods.

    更新日期:2020-01-15
  • Fault detection and diagnosis based on transfer learning for multimode chemical processes
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-15
    Hao Wu; Jinsong Zhao

    Fault detection and diagnosis (FDD) has been an active research field during the past several decades. Methods based on deep neural networks have made some important breakthroughs recently. However, networks require a large number of fault data for training. A chemical process may have several modes during production. Since fault is a low possibility event, some modes may have few fault data in history. Furthermore, collecting and annotating industrial data are extremely expensive and time-consuming. With scarce or unlabeled fault data, networks cannot be effectively used for modeling. In this paper, we present a FDD method based on transfer learning for multimode chemical processes. To overcome the fault data rareness and no label issues in some modes, transfer learning transfers the knowledge from a source mode to a target mode for FDD. Tennessee Eastman (TE) process with five modes is utilized to verify the performance of our proposed method.

    更新日期:2020-01-15
  • Two complementary methods for the computational modeling of cleaning processes in food industry
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-15
    Hannes Deponte; Alberto Tonda; Nathalie Gottschalk; Laurent Bouvier; Guillaume Delaplace; Wolfgang Augustin; Stephan Scholl

    Insufficient cleaning in the food industry can create serious hygienic risks. However, when attempting to avoid these risks, food-processing plants frequently tend to clean for too long, at extremely high temperatures, or with too many chemicals, resulting in high cleaning costs and severe environmental impacts. Therefore, the optimization of cleaning processes in the food industry has significant economic and ecological potential. Unfortunately, in-situ assessments of cleaning processes are difficult, and the multitude of different cleaning situations complicates the definition of a comprehensive approach. In this study, two methodological approaches for the comprehensive modeling of cleaning processes are introduced. The resulting models facilitate comparisons of different cleaning processes and they can be scaled up for processes with similar conditions, using cleaning time as a response. A dimensional analysis is performed to obtain general results and to allow transfer of the approaches to other cleaning situations. The models are established according to the statistical rules for the deduction of multiple regression equations for the prediction of the response based on the input parameters. The terms of the model equation are confirmed with a significance analysis. A machine learning approach is also used to create model equations with symbolic regression. Both methods and the obtained model equations are validated. The two applied approaches reveal similar significant terms and models. Significant dimensionless numbers are the Reynolds number, the density number that describes the ratio of the density of the soil to the density of the cleaning agent, and the soil number, which is a new dimensionless number that characterizes the properties of food soils. The methodology of both approaches is transparent; therefore, the resulting equations can be compared and similarities are found. Both methods are deemed applicable for the computational modeling of cleaning processes in food industry.

    更新日期:2020-01-15
  • Polynomial approximation of inequality path constraints in dynamic optimization
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-14
    Eduardo S. Schultz; Ralf Hannemann-Tamás; Alexander Mitsos

    We propose an algorithm for dynamic optimization problems with inequality path constraints. It solves a sequence of approximated problems where the path constraint is imposed on a finite number of points. Between adjacent points, an approximating polynomial of the constraint value is calculated and an additional constraint is imposed on the maximum value of this polynomial. We consider Taylor and Hermite polynomials. New points are added based on constraint violations or large approximations errors of the approximating polynomials. We prove finite convergence to a feasible point assuming: (i) the dynamic optimization problem has a Slater point, (ii) pointwise constraints are respected at each iteration. We compare the performance of the algorithm with the algorithm by Fu et at. (Automatica 62, 2015, p.184-192) for three small case studies and an up-to-date industrial application where we calculate optimal feed rates for a semi-batch emulsion polymerization reactor. The results show that our proposed algorithm needs to solve fewer subproblems, i.e. fewer iterations, at the cost of more constraints, resulting in smaller CPU times.

    更新日期:2020-01-14
  • Optimal Production and Maintenance Scheduling for a Multiproduct Batch Plant Considering Degradation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-13
    Ouyang Wu; Giancarlo Dalle Ave; Iiro Harjunkoski; Ala Bouaswaig; Stefan Marco Schneider; Matthias Roth; Lars Imsland

    Performance decay due to asset degradation is an important constraint in industrial production and therefore needs to be actively considered. This paper focuses on short-term scheduling for multiproduct batch processes with sequence-dependent degradation and is motivated by a case study in which the sequence of multiple-grade batch runs impacts evolution of fouling. A continuous-time scheduling formulation is proposed to incorporate realistic features of the case study processes. The precedence scheduling concept for the sequential process is employed to model sequences of multiproduct orders and maintenance and is implemented using the general disjunctive programming method. The scheduling formulation is applied to the case study and further analyzed through comprehensive computational tests, which illustrates the efficacy of the proposed formulation.

    更新日期:2020-01-14
  • Nonlinear Process Modelling Using Echo State Networks Optimised by Covariance Matrix Adaption Evolutionary Strategy
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-13
    Kai Liu; Jie Zhang

    Echo state networks (ESN) have been shown to be an effective alternative to conventional recurrent neural networks (RNNs) due to its fast training process and good performance in dynamic system modelling. However, the performance of ESN can be affected by the randomly generated reservoir. This paper presents nonlinear process modelling using ESN optimized by covariance matrix adaption evolutionary strategy (CMA-ES). CMA-ES is used to optimize the structural parameters of ESN: reservoir size, spectral radius, and leak rate. The proposed method is applied to three case studies: modelling a time series, modelling a conic tank, and modelling a fed-batch penicillin fermentation process. The results are compared with those from the original ESN, long short-term memory network, GA-ESN (ESN optimized by genetic algorithm), and feedforward neural networks. It is shown that the proposed method gives much better performance than the original ESN and other networks on all the three case studies.

    更新日期:2020-01-13
  • Machine Learning Refinery Sensor Data to Predict Catalyst Saturation Levels
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-12
    Bram Steurtewagen; Dirk Van Den Poel

    In this research, we propose a novel data-centric way of optimizing a catalytic cracking unit. We first design a soft sensor to predict catalyst saturation levels within a Fluid Catalytic Cracking Unit (FCCU). To achieve this, we implement an established method and combine it with modern algorithms for accurate and robust results. The input for this model is data from a number of sensors throughout the refinery, combined with laboratory data. Catalyst saturation level is measured by way of manual refraction analysis and lookup tables. These manual measurements were combined with laboratory data to provide training input for our soft sensor models. Subsequently, we utilize this new soft sensor model in an input mix optimization in order to continuously optimize the use of the catalyst within the FCCU. This model leads to a higher product yield, less catalyst consumption, and a more efficient process. This proposed optimization pipeline can be introduced as smart process control tying into the development towards Industry 4.0.

    更新日期:2020-01-13
  • Performance evaluation of reverse osmosis brackish water desalination plant with different recycled ratios of retentate
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-11
    A.A. Alsarayreh; M.A. Al-Obaidi; A.M. Al-Hroub; R. Patel; I.M. Mujtaba

    Reverse Osmosis (RO) process has become one of the most widely utilised technologies for brackish water desalination for its capabilities of producing high-quality water. This paper emphasis on investigating the feasibility of implementing the retentate recycle design on the original design of an industrial medium-sized multistage and multi-pass spiral wound brackish water RO desalination plant (1200 m³/day) of Arab Potash Company (APC) located in Jordan. Specifically, this research explores the impact of recycling the high salinity stream of the 1st pass (at different recycled percentages) to the feed stream on the process performance indicators include, the fresh water salinity, overall recovery rate, and specific energy consumption. The simulation is carried out using an earlier model developed by the same authors for the specified RO plant using gPROMS suits. This confirmed the possibility of increasing the product capacity by around 3% with 100% recycle percentage of the high salinity retentate stream.

    更新日期:2020-01-13
  • Design and Control of Distillation Columns with Inert Venting
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-11
    William L. Luyben

    The feed streams in many distillation columns contain small amounts of very light components that must be removed in the overhead system. To avoid having to operate at the high pressures or low temperatures required to totally condense the distillate, a small vapor vent stream is removed from the top of the reflux drum. This paper considers the economic and controllability issues involved in the design and control of these inert venting systems. An important engineering trade-off exists between product losses in the vent and energy consumption leads to an optimum operating pressure, which varies with the concentration of inert in the feed.

    更新日期:2020-01-13
  • Optimal Membrane-Process Design (OMPD): A Software Product for Optimal Design of Membrane Gas Separation Processes
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-10
    Yousef Mohammadi; Takeshi Matsuura; Johannes C. Jansen; Elisa Esposito; Alessio Fuoco; Ludovic F. Dumée; Fausto Gallucci; Enrico Drioli; Masoud Soroush
    更新日期:2020-01-11
  • Integrated Design of Processes and Products: Optimal Renewable Fuels
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-02
    Andrea König; Lisa Neidhardt; Jörn Viell; Alexander Mitsos; Manuel Dahmen
    更新日期:2020-01-02
  • Optimal Estimation of Physical Properties of the Products of an Atmospheric Distillation Column using Support Vector Regression
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-02
    Ahmet Can Serfidan; Firat Uzman; Metin Türkay

    Atmospheric distillation column is one of the most important units in an oil refinery where crude oil is fractioned into its more valuable constituents. Almost all of the state-of-the art online equipment has a time lag to complete the physical property analysis in real time due to complexity of the analyses. Therefore, estimation of the physical properties from online plant data with a soft sensor has significant benefits. In this paper, we estimate the physical properties of the hydrocarbon products of an atmospheric distillation column by support vector regression using Linear, Polynomial and Gaussian Radial Basis Function kernels and SVR parameters are optimized by using a variety of algorithms including genetic algorithm, grid search and non-linear programming. The optimization-based data analytics approach is shown to produce superior results compared to linear regression, the mean testing error of estimation is improved by 5% with SVR 4.01˚C to 3.8˚C.

    更新日期:2020-01-02
  • Introducing KIPET: A novel open-source software package for kinetic parameter estimation from experimental datasets including spectra
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2020-01-02
    C. Schenk; M. Short; J.S. Rodriguez; D. Thierry; L.T. Biegler; S. García-Muñoz; W. Chen

    This paper presents KIPET (Kinetic Parameter Estimation Toolkit) an open-source toolbox for the determination of kinetic parameters from a variety of experimental datasets including spectra and concentrations. KIPET seeks to overcome limitations of standard parameter estimation packages by applying a unified optimization framework based on maximum likelihood principles and large-scale nonlinear programming strategies for solving estimation problems that involve systems of nonlinear differential algebraic equations (DAEs). The software is based on recent advances proposed by Chen et al. (2016) and puts their original framework into an accessible framework for practitioners and academics. The software package includes tools for data preprocessing, estimability analysis, and determination of parameter confidence levels for a variety of problem types. In addition KIPET introduces informative wavelength selection to improve the lack of fit. All these features have been implemented in Python with the algebraic modeling package Pyomo. KIPET exploits the flexibility of Pyomo to formulate and discretize the dynamic optimization problems that arise in the parameter estimation algorithms. The solution of the optimization problems is obtained with the nonlinear solver IPOPT and confidence intervals are obtained through the use of either sIPOPT or a newly developed tool, k_aug. The capabilities as well as ease of use of KIPET are demonstrated with a number of examples.

    更新日期:2020-01-02
  • A Fourier-based control vector parameterization for the optimization of nonlinear dynamic processes with a finite terminal time
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-31
    M. Nadia Pantano; M. Cecilia Fernández; Oscar A. Ortiz; Gustavo J.E. Scaglia; Jorge R. Vega
    更新日期:2019-12-31
  • Wall superheat at the incipient nucleate boiling condition for natural and forced convection: A CFD approach
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-31
    Abdullah Saleem; Shamsuzzaman Farooq; Iftekhar A. Karimi; Raja Banerjee

    Predicting onset of nucleate boiling is important in many process design applications. Analytical models proposed in the literature to predict the wall superheat at incipient nucleate boiling fail to account for different fluid-surface combinations and flow conditions. A Computational Fluid Dynamics (CFD) based analysis is undertaken in this study to formulate fluid independent general correlations to predict the wall superheat at incipient nucleate boiling for natural and forced convection systems. The proposed CFD based correlations encompass different fluid-surface combinations and a wide range of flow conditions. The wall superheat estimates from the developed correlations are in good agreement with available experimental data. Case studies on insulation design to prevent bubble formation in an LNG storage tank and estimating the length of a heating section prior to the advent of nucleate boiling in a heat transfer equipment are presented to demonstrate applicability of the proposed correlations.

    更新日期:2019-12-31
  • Optimal design for sustainable bioethanol supply chain considering the bioethanol production strategies: A case study
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-30
    Masoud Rabbani; Sara Momen; Niloofar Akbarian-Saravi; Hamed Farrokhi-Asl; Zabih Ghelichi

    In search for the promising road transportation fuel, bioethanol has attracted much attention interest during the last few decades. Meanwhile, sewage sludge shows significant potential in the context of biofuel, which motivates policy-makers to commercialize its utilization. To put a step forward in sustainable sewage-based bioethanol production, a multi-objective mixed-integer linear programming model is developed with a solution approach incorporating Best-Worst Method (BWM) approach in an augmented ε-constraint method. The economic viability of the supply chain is analyzed in response to different bioethanol-gasoline blending ratio. To assess the applicability of the model, a case study of Iran has been employed. Computational results illustrate that an increase of 1% in bioethanol-gasoline blending ratio could result in a 54% increase in total supply costs. Ergo, the proposed model can be an efficient tool for the energy sector policymakers to pave the way towards a sustainable world with abundant clean energy.

    更新日期:2019-12-31
  • Separation of the Propane Propylene Mixture With High Recovery by a Dual PSA Process
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-30
    José Antonio Delgado Dobladez; Vicente Ismael Águeda Maté; Silvia Álvarez Torrellas; Marcos Larriba
    更新日期:2019-12-30
  • 更新日期:2019-12-30
  • Importance of Heat-Transfer Area In the Controllability of Openloop Unstable Reactors
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-28
    William L. Luyben

    Openloop unstable reactors present probably the most interesting and challenging control problem in chemical engineering. They can occur when the reactions are exothermic and irreversible and when reactant per-pass conversion is not high so that there is “fuel” available in the reactor to cause a temperature runaway. An openloop unstable process cannot be operated without a feedback controller on automatic. In a typical CSTR process the reactor temperature is controlled by manipulating the flowrate of the cooling medium. Closedloop stability may be achieved if the reactor is designed with sufficient heat-transfer area. The temperature control loop exhibits “conditional stability” since the controller gain must lie inside some range of values whose span depends on the heat-transfer area selected in the design. The purpose of this paper is to demonstrate the critical design trade-off between heat-transfer area and dynamic controllability in openloop unstable CSTRs.

    更新日期:2019-12-29
  • Integration of planning, scheduling and control problems using data-driven feasibility analysis and surrogate models
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-28
    Lisia S. Dias; Marianthi G. Ierapetritou

    In this work, a framework for the integration of planning, scheduling and control using data-driven methodologies is proposed. The framework consists of addressing the integrated problem as a grey-box optimization problem, and using data-driven feasibility analysis and surrogate models to approximate the unknown black-box constraints. We follow a systematic procedure to achieve this integration, consisting of two building blocks: first, we address the integration of scheduling and control followed by the integration of planning and scheduling. To handle dimensionality issues, we introduce the concept of feature selection when building the surrogate models. The methodology is applied to the optimization of an enterprise of air separation plants.

    更新日期:2019-12-29
  • 更新日期:2019-12-27
  • Control Lyapunov-Barrier Function-Based Predictive Control of Nonlinear Processes Using Machine Learning Modeling
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-26
    Zhe Wu; Panagiotis D. Christofides

    Control Lyapunov-Barrier functions (CLBF) have been adopted to design model predictive controllers (MPC) for input-constrained nonlinear systems to ensure closed-loop stability and process operational safety simultaneously. In this work, a CLBF-MPC using an ensemble of recurrent neural network (RNN) models is proposed with guaranteed closed-loop stability and process operational safety for two types of unsafe regions, i.e., bounded and unbounded sets, for nonlinear processes. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example.

    更新日期:2019-12-27
  • An N-Enterprise Investment Game under Risk of Domino Accidents in a Chemical Cluster: Nash and Pareto Equilibria
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-26
    Jun Wu; Hui Yang; Yuan Cheng; Tatsushi Nishi; T.C.E. Cheng

    In a chemical cluster, there are a large number of neighboring enterprises, so the risk of accidents faced by an enterprise depends not only on its risk management strategy but also on those of the others in the cluster. To enhance sustainability, each enterprise can choose one of two investment strategies, namely low-level investment and high-level investment for reducing accidents and the resulting losses. Addressing this investment issue using an N-player game, we show that enterprises taking low-level investment might be underpinned by individual and social rationality theoretically. In this game, the enterprises taking low-level investment can be interpreted as free riders as the enterprises taking high-level investment indirectly protect all the enterprises in the cluster from accidents. By free-riding on the high-level investment enterprises against the domino effect of accidents, each low-level investment enterprise can decrease its accident probability. We find that the low-level investment strategy can be both a Nash equilibrium and a Pareto equilibrium. The maximum number of high-level investment enterprises in the cluster depends on the cluster size, as well as the accidental loss and the domino effect of accidents. We also find that enterprises are inclined to take the high-level investment strategy if the accidental loss becomes larger. With an increasing number of enterprises in the cluster, low-level investment is more attractive. The findings in this paper shed light on the sustainability concept of safety investment for enterprises in a chemical cluster.

    更新日期:2019-12-27
  • Deep hybrid modeling of chemical process: Application to hydraulic fracturing
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-26
    Mohammed Saad Faizan Bangi; Joseph Sang-Il Kwon

    Process modeling began with the use of first principles resulting in ‘white-box’ models which are complex but accurately explain the dynamics of the process. Recently, there has been tremendous interest towards data-based modeling as the resultant ‘black-box’ models are simple, and easy to construct, but their accuracy is highly dependent on the nature and amount of training data used. In order to balance the advantages and disadvantages of ‘white-box’ and ‘black-box’ models, we propose a hybrid model that integrates first principles with a deep neural network, and applied it to hydraulic fracturing process. The unknown process parameters in the hydraulic fracturing process are predicted by the deep neural network and then utilized by the first principles model in order to calculate the hybrid model outputs. This hybrid model is easier to analyze, interpret, and extrapolate compared to a ‘black-box’ model, and has higher accuracy compared to the first principles model.

    更新日期:2019-12-27
  • Global optimization of batch and steady-state recycling chromatography based on the equilibrium model
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-25
    Jana Dienstbier; Johannes Schmölder; Robert Burlacu; Frauke Liers; Malte Kaspereit
    更新日期:2019-12-25
  • An Ingenious Characterization of Reaction Network Using Sub-network Reconstruction
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-24
    Kexin Bi; Chen Zhang; Tong Qiu

    Reaction network model is essential for reactor simulation and kinetic profiling of a complex process. Comprehensive and valid proofs of network mechanism are usually lacking among experimental and simulation studies of complicated reaction process, such as ethylene furnace co-cracking. In this paper, a self-developed Ethylene cracker Simulation and Optimization System (EcSOS) software is applied for co-cracking process numerical simulation and an ingenious characterization method of reaction network is proposed for visualization and deep profiling of the radical reaction network. An effective sub-network reconstruction process is implemented by connecting the substance nodes using actual mass flow and refining key interactions. The product yield evolving trend in simulation and its reason was successfully explained by the reaction network analysis results. The radical reaction mechanism changing obtained by reaction backtracking was systematically sorted out and summarized.

    更新日期:2019-12-25
  • A Two-Stage Procedure for Efficiently Solving the Integrated Problem of Production, Inventory, and Distribution of Industrial Products
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-24
    Mariana E. Cóccola; Carlos A. Méndez; Rodolfo G. Dondo

    This paper deals with the problem of optimally planning the production, inventory and distribution of products transported via multi-compartment vehicles. It assumes that facilities in the distribution network have preservation-storing devices to inventory products on-site. Production activities may be performed on any time period of the planning horizon. Due to problem complexity, a two-stage solution strategy that first generates a set of multi-period distribution routes through a column generation approach is proposed. The routes are used for feeding the MILP formulation of the problem. Several valid inequalities are proposed for expediting the MILP resolution. The aim is to maximize the profit obtained by the company that fabricates and distributes the products. This profit is computed as the total income from sales minus the sum of all costs incurred along the planning horizon. The effectiveness of the two-stage solution strategy is tested on an extensive set of realistic instances.

    更新日期:2019-12-25
  • Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-24
    Rajeevan Arunthavanathan; Faisal Khan; Salim Ahmed; Syed Imtiaz; Risza Rusli

    Fault detection and classifications using supervised learning algorithms are widely studied; however, lesser attention is given to fault detection using unsupervised learning. This work focused on the integration of unsupervised learning with cognitive modelling to detect and diagnose unknown fault conditions. It is achieved by integrating two techniques: i) incremental one class algorithm to identify anomaly condition and introduce a new state of fault to the current fault states if an unknown fault occurs, and ii) dynamic shallow neural network to learn and classify the fault state. The proposed framework is applied to the well-known Tennessee Eastman process and achieved significantly better results compared to results reported by earlier studies. Laboratory experiments are also performed using a pilot-scale system to test the validity of the approach. The results confirm the proposed framework as an effective way to detect and classify known and unknown faults in process operations.

    更新日期:2019-12-25
  • 更新日期:2019-12-25
  • An Optimization Approach for Improving the Exergetic Efficiency in Mesoscale Combustor
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-24
    Xuepu Cao; Yiqing Luo; Xigang Yuan; Zhiwen Qi; Kuo-Tsong Yu

    The optimum designs of combustor are traditionally based on trial and error method. In this paper, we propose a systematic optimization method for the optimum design of this kind of reactor. The optimization procedure is introduced by taking a premixed methane/air combustion as an illustrating example. We show the efficiency of the combustion process is dominated by the entropy generation of the reaction and heat transfer and the viscous dissipation of the fluid flow in the reactor. The minimum of entropy generation of the process is thus taken as the objective function and the conservation equations and constant viscous dissipation as the constraints. A solution procedure for the optimization problem based on the calculus of variation is proposed. To validate the proposed method, we show that the optimization procedure can provide a thermodynamic limit for the combustion process by comparing with different internal structures of combustion chamber.

    更新日期:2019-12-25
  • Comparison of intrusive and nonintrusive polynomial chaos expansion-based approaches for high dimensional parametric uncertainty quantification and propagation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-24
    Jeongeun Son; Yuncheng Du

    We present an uncertainty quantification (UQ) algorithm using the intrusive generalized polynomial chaos (gPC) expansion in combination with dimension reduction techniques and compare the UQ accuracy and computational efficiency of the intrusive gPC-based UQ algorithm to other sampling-based nonintrusive methods. The successful application of intrusive gPC-based UQ is associated with the stochastic Galerkin (SG) projection, which yields a family of models described by several coupled equations of gPC coefficients. Using these coefficients, the evolution of uncertainty in a dynamic system can be quickly determined when there is probabilistic uncertainty in the system. While elegant, when dealing with models that involve complex functions (e.g., nonpolynomial terms) and larger numbers of uncertainties, SG projection becomes computationally intractable and cannot be applied directly to solve gPC coefficients in real-time. To address this issue, the generalized dimension reduction method (gDRM) is used to convert a high-dimensional integral involved in the SG projection into several lower-dimensional integrals that can be easily solved. To show the accuracy of UQ, the algorithm in this work is compared to sampling-based approaches such as the nonintrusive stochastic collocation (SC) and Monte Carlo (MC) simulations using three cases: a nonlinear algebraic benchmark, a penicillin manufacturing process, and autocrine signalling networks of living cells.

    更新日期:2019-12-25
  • Multi-Phase Particle-In-Cell Coupled with Population Balance Equation (MP-PIC-PBE) Method for Multiscale Computational Fluid Dynamics Simulation
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Shinhyuk Kim; Jay H. Lee; Richard D. Braatz

    The ‘multiphase particle-in-cell coupled with population balance equation’ (MP-PIC-PBE) method is introduced for simulating multi-scale multiphase particulate flows. This method couples the meso-scale fluid dynamics simulated by the MP-PIC method with the simulation of the micro-scale particle size distribution. The homogeneous population balance equation is calculated for each discrete particle tracked in a Lagrangian frame, after the MP-PIC numerical procedure is followed at each time instance. This approach allows the particulate phase to accommodate the particulate stresses using spatial gradients and allows the Lagrangian description to predict particle properties by the PBE. For the antisolvent crystallization of Lovastatin in a biradial mixer, the proposed method is compared to an existing method that simulates the spatiotemporal evolution of the particle distribution by combining a multi-environment probability density function with the spatially varying PBE. The MP-PIC-PBE method has lower computational cost and provides more detailed information, such as particle age and location.

    更新日期:2019-12-23
  • Semi-batch reactor control with NMPC avoiding thermal runaway
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Alex Kummer; Tamás Varga; Lajos Nagy

    Several exothermic reactions are carried out in semi-batch reactors (SBR). A not suitable control system can lead to dangerous situations if thermal runaway develops. Reactor runaway can be avoided with application of a non-linear model predictive control (NMPC) with implemented runaway criterion to manipulate the feed flow rate of reagent, although the prediction horizon has to be chosen correctly. For this purpose, process safety time (PST) of the system is determined. Two different operation modes are considered. In the first case the loaded reagent is preheated to avoid accumulation, while in the second mode, only the produced heat by the reactions heat up the reactor. A simple PID controller and the proposed NMPC were tested in both cases. Operation results are compared to each other based on batch times and energy consumptions next to a safe operation. Runaway criteria can be successfully implemented in NMPC for the intensification of SBRs.

    更新日期:2019-12-23
  • Withholding strategies for a conventional and wind generation portfolio in a joint energy and reserve pool market: A gaming-based Approach
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Evangelos G. Tsimopoulos; Michael C. Georgiadis

    This work considers a strategic producer whose generation portfolio consists of conventional and wind power production. Based on the single leader-follower game, a bi-level complementarity model is constructed to derive optimal capacity withholding strategies for this portfolio in a pool-based market. The upper level of the model represents the maximization of the strategic producer’s expected profits while the lower level represents the security-constrained economic dispatch conducted by the independent system operator. The market clearing scheme refers to energy-only markets optimizing jointly scheduled energy and reserves through a two-stage stochastic programming. The first stage illustrates the day-ahead market clearing and the second stage illustrates the balancing market clearing taking into consideration the wind generation uncertainty. With the use of the Karush-Kuhn-Tacker optimality conditions the initial bi-level model is recast into a mathematical programming with equilibrium constraints model which is then reduced into an equivalent mixed integer linear programming using the strong duality theorem and disjunctive constraints. The proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments. It also provides optimal offers based on the endogenous formation of local marginal prices under network constraints and different wind energy penetration levels.

    更新日期:2019-12-23
  • Optimization under uncertainty of the pharmaceutical supply chain in hospitals
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Carlos Franco; Edgar Alfonso-Lizarazo

    In this paper, a simulation-optimization approach based on the stochastic counterpart or sample path method is used for optimizing tactical and operative decisions in the pharmaceutical supply chain. This approach focuses on the pharmacy-hospital echelon, and it takes into account random elements related to demand, prices and the lead times of medicines. Based on this approach, two mixed integer programming (MIP) models are formulated, these models correspond to the stochastic counterpart approximating problems. The first model considers expiration dates, the service level required, perishability, aged-based inventory levels and emergency purchases; the optimal policy support decisions related to the replenishment, supplier selection and the inventory management of medicines. The results of this model have been evaluated over real data and simulated scenarios. The findings show that the optimal policy can reduce the current hospital supply and managing costs in medicine planning by 16% considering 22 types of medicines. The second model is a bi-objective optimization model solved with the epsilon-constraint method. This model determines the maximum acceptable expiration date, thereby minimizing the total amount of expired medicines.

    更新日期:2019-12-23
  • A genetic algorithm approach for parameter estimation in vapour-liquid thermodynamic modelling problems
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Panagiotis Erodotou; Epaminondas Voutsas; Haralambos Sarimveis

    Parameter estimation of semi-empirical models for vapor – liquid equilibrium (VLE) data modelling, plays an important role in the design, optimization, and control of separation units. Conventional optimisation methods are very sensitive to the initial guesses of the unknown parameters and often fail to converge to the global optimum of the parameter estimation nonlinear mathematical programming problems. In this work we present an alternative evolutionary algorithm approach, based on genetic algorithm (GA) technologies, which can solve efficiently the nonlinear parameter estimation problem, finds the global optimum with high probability and most importantly is not sensitive to the initial estimates of the unknown parameters and the tuning parameters of the method. The proposed approach is evaluated and compared with conventional optimisation methods in nine VLE modelling problems of increased complexity. The results illustrate the efficiency and robustness of the proposed method.

    更新日期:2019-12-23
  • Model uncertainty-based evaluation of process strategies during scale-up of biopharmaceutical processes
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-23
    Johannes Möller; Tanja Hernández Rodríguez; Jan Müller; Lukas Arndt; Kim B. Kuchemüller; Björn Frahm; Regine Eibl; Dieter Eibl; Ralf Pörtner

    Reliable scale-up of biopharmaceutical production processes is key in Quality by Design. In this study, a model-based workflow is described to evaluate the bioprocess dynamics during process transfer and scale-up computationally. First, a mathematical model describes the bioprocess dynamics of different state variables (e.g., cell density, titer). Second, the model parameter probability distributions are determined at different scales due to measurement uncertainty. Third, the quantified parameter distributions are statistically compared to evaluate if the process dynamics have been changed. This workflow was tested for the scale-up of an antibody-producing CHO fed-batch process. Significant differences were identified between the process development (30 ml) and implementation (250 ml) scale, and the feeding strategy was validated using model-assisted Design of Experiments. Then, the validated process strategy was successfully scaled up to 2 l laboratory and 50 l pilot scale. In summary, the proposed workflow enables a knowledge-driven evaluation tool for bioprocess development.

    更新日期:2019-12-23
  • 更新日期:2019-12-19
  • A Multi-Commodity Flow Formulation for the Optimal Design of Wastewater Treatment Networks
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-17
    Xin Cheng; Xiang Li

    The wastewater treatment network (WWTN) optimization problem can be viewed as an extension of the generalized pooling problem (GPP), where not only the different streams are mixed and split, but also some chemical components are removed from the system. It is well known that a strong formulation is key to efficient global solution of GPP, and multi-commodity flow formulations have been recognized as such strong formulations. The paper presents the first multi-commodity flow formulation for the WWTN optimization problem. We discuss the challenges in adapting the classical multi-commodity flow approaches for a WWTN, and propose the conditions under which the selected commodities are adequate for modeling a WWTN. We prove that the proposed formulation is at least as strong as a classical component based formulation. We show the significant computational advantage of the proposed formulation over the component based formulation through 16 problem instances from the literature.

    更新日期:2019-12-18
  • Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-14
    Mohammad Yavari; Hamed Zaker

    The effects of disruption in both supply chain and its infrastructures like power network have not been addressed in previous works, simultaneously. Besides, disruption risks have environmental effects besides their economic effects. For this purpose, this paper studies a resilient-green closed loop supply chain design problem considering the perishable nature of products and disruption risks in both supply chain and power networks. To cope with disruptions five different risk mitigation strategies are employed. We have developed an integrated bi-objective mixed-integer linear programming model to minimize the expected total network cost and the expected total amount of carbon emissions of both networks. The results demonstrate that applying strategy of integrating interdependent networks along with four other resilient strategies, especially “intermediate facility” and “lateral transshipment” strategies, will improve the performance of integrated network significantly. Also, with increasing the lifetime of products, the performance of the proposed model will be improved.

    更新日期:2019-12-17
  • Optimal Production Scheduling of Food Process Industries
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-14
    Georgios P. Georgiadis; Borja Mariño Pampín; Daniel Adrián Cabo; Michael C. Georgiadis

    The production scheduling problem of a real-life food industry is addressed in this work. An efficient MILP-based solution strategy is developed to optimize weekly schedules for a Spanish canned fish production plant. The multi-stage, multi-product facility under study consists of both continuous and batch operations resulting in an extremely complex scheduling problem. In order to reduce its computational complexity, an aggregated approach is cleverly proposed, in which the continuous processes are explicitly modeled, while valid feasibility constraints are introduced for the batch stage. Based on this approach, two MILP models are developed, using a mixed discrete-continuous time representation. All technical, operating and design constraints of the facility are considered, while salient characteristics of the canned-food industry, such as assurance of the end products’ microbiological integrity, are aptly modeled. Both the minimization of makespan and changeovers is studied. In order to meet the computational limits imposed by the industry, an order-based decomposition algorithm is further investigated. The method is successfully applied to real-life case studies, generating near-optimal solutions in short CPU times. The suggested solution strategy can be easily extended to consider other real-life scheduling problems from the process industries sector that share similar production characteristics.

    更新日期:2019-12-17
  • Modeling the deactivation of CaO-Based sorbents during multiple Ca-Looping cycles for CO2 post-combustion capture
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-14
    Miguel Abreu; Paula Teixeira; Rui M. Filipe; Luis Domingues; Carla I.C. Pinheiro; Henrique A. Matos
    更新日期:2019-12-17
  • Developing and Validating Linear Dynamic Models for Direct Contact Membrane Distillation During Start-Up Over Wide Operating Conditions
    Comput. Chem. Eng. (IF 3.334) Pub Date : 2019-12-10
    Emad Ali, Jehad Saleh, Jamel Orfi, Abdullah Najib

    This work concerns analyzing, modeling and validation of the dynamic behavior of a Direct Contact Membrane Distillation (DCMD) pilot-plant. The reaction curve method is used to analyze the dynamic characteristics of the outlet permeate and brine temperatures. The time constant for these two process outputs were extracted from the experimental data and analyzed. A low order and high-order linear models in the form of state-space and transfer functions were developed and used to approximate the dynamic of the permeate and brine temperatures, respectively. It is found that these linear dynamic models cannot fully describe the physical process behavior over wide operating conditions. The global deviation between the measured and simulated transient response of the brine temperature and permeate temperature over the entire operating conditions is found to be 15% and 24%, respectively. Hence, there is a potential to improve the dynamic model effectiveness by incorporating the nonlinearity aspects of the real process.

    更新日期:2019-12-11
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