Special Issue "Synergies in Combined Development of Processes and Models"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Computational Methods".

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editors

Prof. Dr. Jose Diaz
Website
Guest Editor
Energy Department, University of Oviedo. c/ Wifredo Ricart, 33204 Gijón, Spain
Interests: steelmaking; energy; process engineering; mathematical modelling; applied heat
Special Issues and Collections in MDPI journals
Prof. Dr. Francisco Javier Fernández García
Website
Guest Editor
Energy Department, University of Oviedo. c/ Wifredo Ricart, 33204 Gijón, Spain
Interests: heat transfer in industrial processes; energy efficiency; energy in buildings; heat recovery; thermodynamic analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Ignacio Alvarez
Website
Guest Editor
Department of Electrical Engineering, University of Oviedo, Campus de Viesques s/n, Gijón 33204, Spain
Interests: on-line signal acquisition and processing for industrial processes
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

Mathematical modeling and processes have often evolved together, but nowadays they are more and more interdependent. The digital revolution has sped up this tendency, producing a huge amount of process data through ubiquitous input–output devices and smarter processing techniques. Therefore, the ever increasing demand for more efficient, more environmental-friendly, and safer processes should lead us, researchers and process engineers, to take full advantage of this challenging scenario through the following:

  • A multidisciplinary approach;
  • An enhanced process understanding;
  • Comparative analyses of process–model alternatives;
  • The long-term stability of models.

This Special Issue on “Synergies in the Combined Development of Processes and Models” aims to gather novel and relevant research on synergetic advances in processes and modeling in order to achieve more efficient use of materials and energy, as well as reduced emissions. Contributions may pertain to any aspect of the process engineering field: equipment and facilities, operating procedures, measurement systems, materials, energies, etc., in connection with mathematical modeling, for a better process understanding, optimization, and control. Topics include but are not limited to the following:

  • A sensitivity analysis of processes, models, and meta-models;
  • The combined development of sensors and models for improved process efficiency;
  • Hybrid, multivariate, data-driven, adaptive models with long-term stability;
  • Simulation in process design, process understanding, and process optimization;
  • Advanced human–machine interfaces and simulators;
  • Success cases of the combined development of processes and models in the industry;
  • The assessment of synergies between the existing process and advanced modeling techniques.
Prof. Jose Diaz
Prof. Dr. Francisco Javier Fernández García
Prof. Dr. Ignacio Alvarez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2020 an APC of 1500 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process control
  • process optimization
  • data-driven modeling
  • mechanistic modeling
  • adaptive models
  • numerical simulation
  • sensitivity analysis
  • advanced sensors
  • human-machine interfaces

Published Papers (12 papers)

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Research

Open AccessFeature PaperArticle
A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting
Processes 2020, 8(5), 617; https://doi.org/10.3390/pr8050617 - 21 May 2020
Abstract
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this [...] Read more.
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
A Novel Process of H2/CO2 Membrane Separation of Shifted Syngas Coupled with Gasoil Hydrogenation
Processes 2020, 8(5), 590; https://doi.org/10.3390/pr8050590 - 15 May 2020
Abstract
A novel process of membrane separation for H2/CO2 of shifted syngas coupled with gasoil hydrogenation (NMGH) is proposed. First, a new process, with two-stage CO2-selective and one-stage H2-selective membranes, was developed to substitute the conventional PSA [...] Read more.
A novel process of membrane separation for H2/CO2 of shifted syngas coupled with gasoil hydrogenation (NMGH) is proposed. First, a new process, with two-stage CO2-selective and one-stage H2-selective membranes, was developed to substitute the conventional PSA separation devices to remove CO2 and purify H2 in coal gasification refineries to reduce energy consumption and investment costs. Then, the process was coupled with gasoil hydrogenation and the recycled H2 produced by the hydrogenation reactor could be further purified by the H2-selective membrane, which increased the H2 concentration of the hydrogenation reactor inlet by about 11 mol.% compared with the conventional direct recycling process, and the total system pressure was reduced by about 2470 kPa. At the same time, this additional membrane separation and purification prevented the accumulation of CO/CO2 in the recycled H2, which ensured the activity of the catalyst in the reactor and the long-term stable operation of the devices. Further, parameters such as compressor power, PI (polyimide)/PEO (polyethylene oxide) membrane area, pressure ratio on both sides of the membrane, and purity of make-up H2 were optimized by sensitivity analysis. The results showed that, compared with the conventional method, the NMGH process simplified operations, significantly reduced the total investment cost by $17.74 million, and lowered the total annual costs by $1.50 million/year. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Improvement of Small Wind Turbine Control in the Transition Region
Processes 2020, 8(2), 244; https://doi.org/10.3390/pr8020244 - 21 Feb 2020
Abstract
Wind energy conversion systems are very challenging from the control system viewpoint. The control difficulties are even more challenging when wind turbines are able to operate at variable speed and variable pitch. The contribution of this work is focused on designing a combined [...] Read more.
Wind energy conversion systems are very challenging from the control system viewpoint. The control difficulties are even more challenging when wind turbines are able to operate at variable speed and variable pitch. The contribution of this work is focused on designing a combined controller that significantly alleviates the wind transient loads in the power tracking and power regulation modes as well as in the transition zone. In a previous work, the authors studied the applicability of different multivariable decoupling methodologies. The methodologies were tested in simulation and verified experimentally in a lab-scale wind turbine. It was demonstrated that multivariable control strategies achieve a good closed-loop response within the transition region, where the interaction level is greater. Nevertheless, although such controllers showed an acceptable performance in the power tracking (region II) and power regulation (region IV) zones, appreciable improvement was possible. To this end, the new proposed methodology employs a multivariable gain-scheduling controller with a static decoupling network for the transition region and monovariable controllers for the power tracking and power regulation regions. To make the transition between regions smoother, a gain scheduling block is incorporated into the multivariable controller. The proposed controller is experimentally compared with a standard switched controller in the lab-scale wind turbine. The experiments carried out suggest that the combination of the proposed multivariable strategy for the transition region to mitigate wind transient loads combined with two monovariable controllers, one dedicated to region II and other to region IV, provide better results than traditional switched control strategies. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessFeature PaperArticle
Phenomenological Analysis of Thermo-Mechanical-Chemical Properties of GFRP during Curing by Means of Sensor Supported Process Simulation
Processes 2020, 8(2), 192; https://doi.org/10.3390/pr8020192 - 05 Feb 2020
Abstract
Inherent process-induced deformations (PID) and residual stresses impede the application of composite parts. PID lead to a geometrical mismatch in assemblies and require subsequent work for tolerance compensation. Unknown residual stresses cause overweighted structures resulting from unnecessary high safety factors. To counteract the [...] Read more.
Inherent process-induced deformations (PID) and residual stresses impede the application of composite parts. PID lead to a geometrical mismatch in assemblies and require subsequent work for tolerance compensation. Unknown residual stresses cause overweighted structures resulting from unnecessary high safety factors. To counteract the deformations, the tool design needs to be modified until the component geometry meets the specifications. This process is mostly carried out empirically and is time and cost intensive. To improve the efficiency of the development process, an in-deep comprehension of the manufacturing processes is mandatory. Therefore, experimental and simulation-based methods are increasingly applied and enhanced. The object of this work is to investigate the development of process-induced strains as well as the material behaviour during the manufacturing for a GFRP plate. The process-induced strains are monitored by optical fiber Bragg grating (FBG) sensors. The change of the material phases is detected by dielectric sensors. Furthermore, a detailed process simulation considering viscoelastic effects and reaction kinetics is performed. Finally, the measurements are correlated with the simulation data to validate the simulation approach. A very good correlation for both the reaction kinetics as well as the process-induced strains is observed. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Modeling of Parallel Movement for Deep-Lane Unit Load Autonomous Shuttle and Stacker Crane Warehousing Systems
Processes 2020, 8(1), 80; https://doi.org/10.3390/pr8010080 - 07 Jan 2020
Abstract
The autonomous shuttle and stacker crane (AC/SC) warehousing system, as a new automated deep-lane unit load storage/retrieval system, has been becoming more popular, especially for batch order fulfilment because of its high flexibility, low operational cost and improved storage capacity. This system consists [...] Read more.
The autonomous shuttle and stacker crane (AC/SC) warehousing system, as a new automated deep-lane unit load storage/retrieval system, has been becoming more popular, especially for batch order fulfilment because of its high flexibility, low operational cost and improved storage capacity. This system consists of a shuttle sub-system that controls motion along the x-axis and a stacker crane sub-system that controls motion along the y-axis and z-axis. The combination of shuttles and a stacker crane performs storage and retrieval tasks. Modelling the parallel motion is an important design tool that can be used to calculate the optimal number of shuttles for a given configuration of the warehousing system. In this study, shuttle movements from one lane to another are inserted into the stock-keeping unit (SKU) task queue, and convert such that they are consistent with the retrieval tasks. The tasks are then grouped according to their starting lane, and converted to an assembly-line parallel job problem by analysing the operating mode with the objectives of minimising the total working time of the stacker crane and the wasted shuttle time. A time sequence mathematical model based on the motion of the shuttles and stacker crane is proposed, and an improved Pareto-optimal elitist non-dominated sorting genetic algorithm is used to solve this multi-objective optimization problem. The model is validated via a simulation study, and via a real-world warehousing case study. We go on to describe guidelines for the layout and configuration of AS/SC warehousing systems, including the optimal number of shuttles and number of x-axis storage cells of lanes, which can improve efficiency and minimise both capital investment and operating costs. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms
Processes 2020, 8(1), 76; https://doi.org/10.3390/pr8010076 - 07 Jan 2020
Abstract
IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user’s computing demands. Customers of such services [...] Read more.
IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user’s computing demands. Customers of such services would like to adjust the utilization policy promptly by considering the charge of the service, but an instantaneous response is not possible as it takes several hours or even a couple of days for cloud service providers to inform the billing information. In this article, we present an instant cost estimation model for estimating the cost of public cloud resources. Specifically, our model estimates the cost of IaaS by monitoring the usage of resources on behalf of virtual machine instances. As this is performed by generating a user-side metering daemon, it is very precise and thus similar to the resource usage evaluated by the cloud service provider. To validate our model, we run PC laboratory services for 50 students in two classes by making use of a public cloud during a semester. Experimental results show that the accuracy of our model is over 99.3% in comparison with the actual charge of the public cloud. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Hybrid Integrations of Value Stream Mapping, Theory of Constraints and Simulation: Application to Wooden Furniture Industry
Processes 2019, 7(11), 816; https://doi.org/10.3390/pr7110816 - 05 Nov 2019
Abstract
This paper studies manufacturing processes in a wooden furniture manufacturing company. The company suffers from long manufacturing lead times and an unbalanced production line. To identify sources of waste and delay value stream mapping (VSM) and a discrete event simulation model is implemented. [...] Read more.
This paper studies manufacturing processes in a wooden furniture manufacturing company. The company suffers from long manufacturing lead times and an unbalanced production line. To identify sources of waste and delay value stream mapping (VSM) and a discrete event simulation model is implemented. VSM is used to visualize and analyze the major processes of the company and provide quantifiable KPIs; the manufacturing lead-time and then Overall Equipment Effectiveness (OEE) settings. A discrete event simulation model is then built to analyze the company on a wider scale and provide the data required to identify bottlenecks. Building on the data gathered from the production lines and the simulation model, two-bottleneck detection methods are used, the utilization method, and the waiting time method. Then based on the comparison of the two methods a third bottleneck detection is utilized; the scenario-based method, to identify the primary and secondary bottlenecks. After the bottlenecks are identified, changes are then evaluated using the simulation model and radar charts were built based on the improved simulation model, which evaluates the effect of changes in the utilization and OEE results. This work managed to neutralize the effect of one of the main bottlenecks and minimize the effect of the other. The manufacturing utilization was increased by 15.8% for the main bottleneck resources followed by 2.4% for the second one. However, it is hard to convince the traditional administration of this small size manufacturing plant to adopt a completely revolutionizing, costly, and risky (at such level) lean manufacturing approach. This paper studies and provides a much lower in cost and verified scheme of enhancement. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Optimization of Microwave Coupled Hot Air Drying for Chinese Yam Using Response Surface Methodology
Processes 2019, 7(10), 745; https://doi.org/10.3390/pr7100745 - 15 Oct 2019
Cited by 1
Abstract
The effect of microwave coupled hot air drying on rehydration ratio (RR) and total sugar content (TSC) of Chinese yam was investigated. Single factor test and response surface methodology were used for process parameter optimization with hot air temperature, hot air velocity, slice [...] Read more.
The effect of microwave coupled hot air drying on rehydration ratio (RR) and total sugar content (TSC) of Chinese yam was investigated. Single factor test and response surface methodology were used for process parameter optimization with hot air temperature, hot air velocity, slice thickness, and microwave power density as variables and RR and TSC of dried products as responses. The effect of variables on RR followed the order: slice thickness > hot air temperature > microwave power density > hot air velocity. The effect of variables on TSC followed the order: slice thickness > microwave power density > hot air velocity > hot air temperature. The optimized process parameters were hot air velocity of 2.5 m/s, hot air temperature of 61.7 °C, slice thickness of 8.5 mm, and microwave power density of 5.9 W/g. Under the optimal conditions, the predicted values of RR and TSC were 1.90 g/g and 5.74 g/100 g, respectively, which is very close to corresponding actual values (1.83 g/g and 5.72 g/100 g). The desirability of 0.913 further validated the effectiveness of the model. The findings from this work may apply to other agricultural products. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Design of S2N—NEWMA Control Chart for Monitoring Process having Indeterminate Production Data
Processes 2019, 7(10), 742; https://doi.org/10.3390/pr7100742 - 14 Oct 2019
Abstract
The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic [...] Read more.
The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Sustainable Personnel Scheduling Problem Optimization in a Natural Gas Combined-Cycle Power Plant
Processes 2019, 7(10), 702; https://doi.org/10.3390/pr7100702 - 05 Oct 2019
Cited by 1
Abstract
This paper deals with a sustainable personnel scheduling problem of personnel working in a large-scale natural gas combined-cycle power plant in Turkey. The proposed model focuses on employee complaints due to unfair work schedules and the results of balanced assignments based on power [...] Read more.
This paper deals with a sustainable personnel scheduling problem of personnel working in a large-scale natural gas combined-cycle power plant in Turkey. The proposed model focuses on employee complaints due to unfair work schedules and the results of balanced assignments based on power plant interruptions. Eighty personnel work in three shifts at this natural gas combined-cycle power plant. The model is solved with respect to some of the workers’ skills, and there are 20 criteria regarding skills. The analytic network process method is used to get the weights of workers’ skills, which are calculated and included in the model. Goal programming is used in this paper. Our proposed model gives cost minimization and fair work schedules for the power plant. Compared with the literature, the number and set of criteria are unique in terms of personnel competency in the energy sector. Minimizing cost and imbalanced assignments was achieved by the proposed model for the first time without considering the sector. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process
Processes 2019, 7(7), 415; https://doi.org/10.3390/pr7070415 - 02 Jul 2019
Abstract
In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters [...] Read more.
In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters of the RTD-A controller. Its performance is later compared with that of the conventional standard proportional integral derivative controller. The gain scheduled RTD-A controller is designed and implemented on a nonlinear conical tank process. Also, various performances attributes such as the integral square error, integral absolute error, integral time absolute error, rise time, and settling time are calculated for the first-order process and conical tank process. The servo responses with RTD-A are also compared against the responses recorded from the conventional control schemes. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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Open AccessArticle
Numerical Determination of RVE for Heterogeneous Geomaterials Based on Digital Image Processing Technology
Processes 2019, 7(6), 346; https://doi.org/10.3390/pr7060346 - 06 Jun 2019
Cited by 1
Abstract
Representative volume element (RVE) is an important parameter in numerical tests of mechanical properties of heterogeneous geomaterials. For this study, a digital image processing (DIP) technology was proposed for estimating the RVE of heterogeneous geomaterials. A color image of soil and rock mixture [...] Read more.
Representative volume element (RVE) is an important parameter in numerical tests of mechanical properties of heterogeneous geomaterials. For this study, a digital image processing (DIP) technology was proposed for estimating the RVE of heterogeneous geomaterials. A color image of soil and rock mixture (SRM) with size of 400 × 400 mm2 taken from a large landslide was used to illustrate the determination procedure of the SRM. Six sample sizes ranging from 40 × 40 mm2 to 240 × 240 mm2 were investigated, and twelve random samples were taken from the binarized image for each sample size. A connected-component labeling algorithm was introduced to identify the microstructure. After establishing the numerical finite difference models of the samples, a set of numerical triaxial tests under different confining pressures were carried out. Results show that the size of SRM sample affects the estimation of the mechanical properties, including compressive strength, cohesion, and internal friction angle. The larger the size of the samples, the less variability of the estimated mechanical properties. The coefficient of variation (CV) was applied to measure the variability of mechanical properties, and the RVE of the SRM was determined easily with a predefined acceptance threshold of the CV. The results show that a DIP-based modeling method is an effective method got the RVE determination of heterogeneous geomaterials. Full article
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
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