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Special Issue "Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools"

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 68636

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Special Issue Editors

Prof. Dr. Luis Puigjaner

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Guest Editor

Center for Process and Environment Engineering (CEPIMA), Chemical Engineering Department, EEBE- c, Universitat Politècnica de Catalunya (UPC), Eduard Maristany 10-14, Ed. I-5, 08019 Barcelona, Spain
Interests: process systems engineering (PSE); chemical processes; process design, synthesis, modelling, simulation, optimization; linear & non-linear processes; batch processes; intelligent manufacturing; knowledge management; circular economy principles in process systems; industrial symbiosis; integrated and sustainable supply-chain planning, scheduling, operational; PSE vs Intelligent manufacturing; knowledge-based recipe management for production processes; sustainable bio-based energy supply chains

Prof. Dr. Antonio Espuña Camarasa

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Guest Editor

Department of Chemical Engineering, Universitat Politècnica de Catalunya (UPC), E-08019 Barcelona, Spain
Interests: process systems engineering; sustainability and circular economy; artificial intelligence tools for decision-making support
Special Issues, Collections and Topics in MDPI journals

Dr. Edrisi Muñoz Mata

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Guest Editor

Chemical Engineering Department, EEBE, Universitat Politècnica de Catalunya, Av. Eduard Maristany, 10-14, 08019 Barcelona, Spain
Interests: Formal Knowledge Models; domain ontology development; computational intelligence; process intelligent systems; decision support systems; improved transactional data systems

Dr. Elisabet Capón García
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Guest Editor

ABB Switzerland Ltd, 5400 Baden, Switzerland
Interests: process optimization; mathematical programming; process simulation; artificial intelligence; discrete optimization; machine learning; advanced manufacturing

Special Issue Information

Dear Colleagues,

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. Such an integrated approach also incorporates information coming from the local basic control and supervisory modules into the scheduling/planning formulation, making it possible to dynamically react to incidents occurring in the network components at the appropriate level of decision-making.

The use of a wide-integrated solution should allow enhanced coordination and cooperation between network components by avoiding competition among them, eventually leading to local optima and inefficiency associated with inconsistent isolated decisions at different levels. Such a wide-integrated solution approach would be providing new structural alternatives, more effective management policies, more economical design options. Moreover, the solution obtained can work in practice requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, thus leading to reduced cost, waste, energy consumption, and environmental impact and also to increased benefits.

More recently, the exploitation of new technology integration such as through semantic models in the form of formal knowledge models allows capturing and utilizing domain knowledge, human knowledge, and expert knowledge towards wide intelligent management. Otherwise, the development of advanced technologies and tools such as cyberphysical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc. have captured the attention of manufacturing enterprises towards smart manufacturing systems. This Special Issue also calls for contributions from these advanced areas.

In summary, we look for articles addressing (but not limited to) the following concepts:

Development of advanced mathematical models and methodologies for the integrated approach of:
- The network design problem, such as the location of the plant, warehouses, and distribution centers, capacity and technology selection, etc.;
- The supply chain planning problem, including distribution planning, inventory control, and product demand forecasting;
- Integration of production, financial and environmental aspects, risk, and uncertainty.

The expected models will tackle a multiobjective view of achieving the necessary trade-off between often contradictory benefits in terms of economic, environmental, customer satisfaction, and increased response to dynamic market changes;

Development of detailed production scheduling at plant level for batch, continuous and discrete manufacturing for online scheduling implemented in practice under real-time variations and uncertainty;
Integration of the tracking system of network dynamics within the holistic decision-making model (e.g., by enclosing a model predictive control framework), thus facilitating equipment capacity handling similarly at strategic and operational levels and enabling adequate response to incidents for enhanced production sustainability;
Development of suitable frameworks and algorithms for solving these problems in an efficient and integrated manner (e.g., surrogate problem decomposition, disjunctive programming, Lagrange decomposition?);
Development of software prototypes for the implementation of the above methodologies and algorithms, illustrating their applicability in several real-life industrial case studies involving typical manufacturing/distribution networks belonging to relevant sectors in the world;
Development of novel frameworks focusing on the utilization of formal knowledge models, facilitating new technologies implementation, and transactional system integration;
Further development of smart manufacturing systems for the transformation of manufacturing enterprises, from traditional to the intellectualized ones;
Development of Intelligent Systems and Intelligent Agents focused on cooperative work between human beings and computers, enhancing the capability of human decision-making and problem solutions in the process engineering field.

Prof. Dr. Luis Puigjaner
Prof. Dr. Antonio Espuña Camarasa
Dr. Edrisi Muñoz Mata
Dr. Elisabet Capón García
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 submissions that pass pre-check are 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 2000 CHF (Swiss Francs). 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

Enterprise-wide integrated modeling, multi-scale optimization
Advanced mathematical models and methodologies, PSE approach
Integration of production, financial, environmental, risk, and uncertainty
Integration of network design and supply chain
Detailed production and online scheduling
Integration of tracking system of network dynamics
Enhancing production sustainability
Development of suitable frameworks and algorithms
Software prototypes for real industrial applications
Development of formal knowledge models development
Transactional systems integration
Advanced smart manufacturing systems
Cooperative intelligent systems and intelligent agents
Use of new tools, IoT, Big data, cloud computing, cyberphysical systems

Published Papers (22 papers)

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Editorial

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Open AccessEditorial

Expanding the Horizons of Manufacturing, towards Wide Integration, Smart System, and Tools

Luis Puigjaner

Antonio Espuña

Edrisi Muñoz

and

Elisabet Capón-García

Processes 2022, 10(4), 772; https://doi.org/10.3390/pr10040772 - 14 Apr 2022

Viewed by 784

Abstract

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

Research

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Open AccessEditor’s ChoiceArticle

Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer

Sheng-Long Jiang

Lazaros G. Papageorgiou

Ian David L. Bogle

and

Vassilis M. Charitopoulos

Processes 2022, 10(3), 454; https://doi.org/10.3390/pr10030454 - 24 Feb 2022

Cited by 2 | Viewed by 1081

Abstract

Market globalisation, shortened patent lifetimes and the ongoing shift towards personalised medicines exert unprecedented pressure on the pharmaceutical industry. In the push for continuous pharmaceutical manufacturing, processes need to be shown to be agile and robust enough to handle variations with respect to product demands and operating conditions. In this paper we examine the use of operational envelopes to study the trade-off between the design and operational flexibility of the fluid bed dryer at the heart of a tablet manufacturing process. The operating flexibility of this unit is key to the flexibility of the full process and its supply chain. The methodology shows that for the fluid bed dryer case study there is significant effect on flexibility of the process at different drying times with the optimal obtained at 700 s. The flexibility is not affected by the change in volumetric flowrate, but only by the change in temperature. Here the method used a black box model to show how it could be done without access to the full model equation set, as this often needs to be the case in commercial settings. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Research on Measurement and Application of China’s Regional Logistics Development Level under Low Carbon Environment

and

Processes 2021, 9(12), 2273; https://doi.org/10.3390/pr9122273 - 17 Dec 2021

Cited by 2 | Viewed by 1450

Abstract

To solve the problem of fuzziness and randomness in regional logistics decarbonization evaluation and accurately assess regional logistics decarbonization development, an evaluation model of regional logistics decarbonization development is established. First, the evaluation index of regional logistics decarbonization development is constructed from three dimensions: low-carbon logistics environment support, low-carbon logistics strength and low-carbon logistics potential. Second, the evaluation indexes are used as cloud model variables, and the cloud numerical characteristic values and cloud affiliation degrees are determined according to the cloud model theory. The entropy weight method is used to determine the index weights, and the comprehensive determination degree of the research object affiliated to the logistics decarbonization level is calculated comprehensively. Finally, Beijing-Tianjin-Hebei region is used as an example for empirical evidence, analyzing the development logistics decarbonization and its and temporal variability in Beijing, Tianjin and Hebei provinces and cities. The results of the study show that the development logistics decarbonization in Beijing, Tianjin and Hebei Province has been improved to different degrees during 2013–2019, but the development is uneven. Developing to 2019, the three provinces and cities of Beijing, Tianjin and Hebei still have significant differences in terms of economic environment, logistics industry scale, logistics industry inputs and outputs, and technical support. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperEditor’s ChoiceArticle

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

and

Gintaras V. Reklaitis

Processes 2021, 9(9), 1612; https://doi.org/10.3390/pr9091612 - 08 Sep 2021

Cited by 7 | Viewed by 1450

Abstract

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Multi Set-Point Explicit Model Predictive Control for Nonlinear Process Systems

Vassilis M. Charitopoulos

Lazaros G. Papageorgiou

and

Vivek Dua

Processes 2021, 9(7), 1156; https://doi.org/10.3390/pr9071156 - 02 Jul 2021

Cited by 4 | Viewed by 1643

Abstract

In this article, we introduce a novel framework for the design of multi set-point nonlinear explicit controllers for process systems engineering problems where the set-points are treated as uncertain parameters simultaneously with the initial state of the dynamical system at each sampling instance. To this end, an algorithm for a special class of multi-parametric nonlinear programming problems with uncertain parameters on the right-hand side of the constraints and the cost coefficients of the objective function is presented. The algorithm is based on computed algebra methods for symbolic manipulation that enable an analytical solution of the optimality conditions of the underlying multi-parametric nonlinear program. A notable property of the presented algorithm is the computation of exact, in general nonconvex, critical regions that results in potentially great computational savings through a reduction in the number of convex approximate critical regions. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model

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Processes 2021, 9(6), 1080; https://doi.org/10.3390/pr9061080 - 21 Jun 2021

Cited by 2 | Viewed by 1465

Abstract

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations

Sujeong Baek

and

Dong Oh Kim

Processes 2021, 9(4), 634; https://doi.org/10.3390/pr9040634 - 05 Apr 2021

Cited by 3 | Viewed by 1370

Abstract

In manufacturing systems, pick-up operations by vacuum grippers may fail owing to manufacturing errors in an object’s surface that are within the allowable tolerance limits. In such situations, manual interference is required to resume system operation, which results in considerable loss of time as well as economic losses. Although vacuum grippers have many advantages and are widely used in the industry, it is highly difficult to directly monitor the current machine status and provide appropriate recovery feedback for stable operation. Therefore, this paper proposes a method to detect the success or failure of a suction operation in advance by analyzing the amount of outlet air pressure in the Venturi line. This was achieved by installing an air pressure sensor on the Venturi line to predict whether the current suction action will be successful. Through empirical experiments, it was found that downward movements in the z-axis of the vacuum gripper can easily rectify a faulty gripper suction operation. Real-time monitoring results verified that predictive process adjustment of the pick-up operation can be performed by modifying the z-position of the vacuum gripper. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

A Systematic Model for Process Development Activities to Support Process Intelligence

Edrisi Muñoz

Elisabet Capon-Garcia

Enrique Martinez Muñoz

and

Luis Puigjaner

Processes 2021, 9(4), 600; https://doi.org/10.3390/pr9040600 - 30 Mar 2021

Cited by 3 | Viewed by 1656

Abstract

Process, manufacturing, and service industries currently face a large number of non-trivial challenges ranging from product conception, going through design, development, commercialization, and delivering in a customized market’s environment. Thus, industries can benefit by integrating new technologies in their day-by-day tasks gaining profitability. This work presents a model for enterprise process development activities called the wide intelligent management architecture model to integrate new technologies for services, processes, and manufacturing companies who strive to find the most efficient way towards enterprise and process intelligence. The model comprises and structures three critical systems: process system, knowledge system, and transactional system. As a result, analytical tools belonging to process activities and transactional data system are guided by a systematic development framework consolidated with formal knowledge models. Thus, the model improves the interaction among processes lifecycle, analytical models, transactional system, and knowledge. Finally, a case study is presented where an acrylic fiber production plant applies the proposed model, demonstrating how the three models described in the methodology work together to reach the desired technology application life cycle assessment systematically. Results allow us to conclude that the interaction between the semantics of formal knowledge models and the processes-transactional system development framework facilitates and simplifies new technology implementation along with enterprise development activities. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

MINLP Model for Operational Optimization of LNG Terminals

Zhencheng Ye

Xiaoyan Mo

and

Liang Zhao

Processes 2021, 9(4), 599; https://doi.org/10.3390/pr9040599 - 30 Mar 2021

Cited by 9 | Viewed by 2117

Abstract

Liquefied natural gas (LNG) is a clear and promising fossil fuel which emits less greenhouse gas (GHG) and has almost no environmentally damaging sulfur dioxide compared with other fossil fuels. An LNG import terminal is a facility that regasifies LNG into natural gas, which is supplied to industrial and residential users. Modeling and optimization of the LNG terminals may reduce energy consumption and GHG emission. A mixed-integer nonlinear programming model of the LNG terminal is developed to minimize the energy consumption, where the numbers of boil-off gas (BOG) compressors and low-pressure (LP) pumps are considered as integer variables. A case study from an actual LNG terminal is carried out to verify the practicality of the proposed method. Results show that the proposed approach can decrease the operating energy consumption from 9.15% to 26.1% for different seasons. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Ontology-Based Process Modelling-with Examples of Physical Topologies

Heinz A Preisig

Processes 2021, 9(4), 592; https://doi.org/10.3390/pr9040592 - 29 Mar 2021

Cited by 8 | Viewed by 1761

Abstract

Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic approach to process modelling. We do not aim toward a single modelling domain, but we enable to address specific application domains and object inheritance. We start with reductionism and demonstrate how the basic entities are depending on the targeted application domain. We use directed graphs to capture process models, and we introduce a new concept, which we call ”tokens” that enables us to extend the context beyond physical systems. The network representation is hierarchical so as to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy, making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a network of networks. We identify two distinct network communication ports, namely ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behaviour equations. Linking the behaviours to the basic entities defines the alphabet of a graphical language. We use this graphical language to represent processes, which has proven to be efficient and valuable. A set of three examples demonstrates the power of the graphical language. The Process Modelling framework (ProMo) implements the ontology-centred approach to process modelling and uses the graphical language to construct process models. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Algorithmic Approaches to Inventory Management Optimization

and

Processes 2021, 9(1), 102; https://doi.org/10.3390/pr9010102 - 06 Jan 2021

Cited by 16 | Viewed by 6862

Abstract

An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic simulation environment that was built upon the open-source OR-Gym Python package. The results indicate that, of the three approaches, stochastic modeling yields the largest increase in profit, whereas reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies that are comparable to reinforcement learning in terms of their profitability. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework

Zeinab Shahbazi

and

Yung-Cheol Byun

Processes 2021, 9(1), 92; https://doi.org/10.3390/pr9010092 - 04 Jan 2021

Cited by 22 | Viewed by 2896

Abstract

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Analysis and Optimization of Two Film-Coated Tablet Production Processes by Computer Simulation: A Case Study

and

Processes 2021, 9(1), 67; https://doi.org/10.3390/pr9010067 - 30 Dec 2020

Cited by 1 | Viewed by 2145

Abstract

Increasing regulatory demands are forcing the pharmaceutical industry to invest its available resources carefully. This is especially challenging for small- and middle-sized companies. Computer simulation software like FlexSim allows one to explore variations in production processes without the need to interrupt the running process. Here, we applied a discrete-event simulation to two approved film-coated tablet production processes. The simulations were performed with FlexSim (FlexSim Deutschland—Ingenieurbüro für Simulationsdienstleistung Ralf Gruber, Kirchlengern, Germany). Process visualization was done using Cmap Tools (Florida Institute for Human and Machine Cognition, Pensacola, FL, USA), and statistical analysis used MiniTab^® (Minitab GmbH, Munich, Germany). The most critical elements identified during model building were the model logic, operating schedule, and processing times. These factors were graphically and statistically verified. To optimize the utilization of employees, three different shift systems were simulated, thereby revealing the advantages of two-shift and one-and-a-half-shift systems compared to a one-shift system. Without the need to interrupt any currently running production processes, we found that changing the shift system could save 50–53% of the campaign duration and 9–14% of the labor costs. In summary, we demonstrated that FlexSim, which is mainly used in logistics, can also be advantageously implemented for modeling and optimizing pharmaceutical production processes. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Abrasive Water Jet Cutting of Hardox Steels—Quality Investigation

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Processes 2020, 8(12), 1652; https://doi.org/10.3390/pr8121652 - 14 Dec 2020

Cited by 15 | Viewed by 1618

Abstract

The paper aims to study the surface quality dependency on selected parameters of cuts made in Hardox™ by abrasive water jet (AWJ). The regression process was applied on measured data and the equations were prepared for both the Ra and Rz roughness parameters. One set of regression equations was prepared for the relationship of Ra and Rz on cutting parameters—pumping pressure, traverse speed, and abrasive mass flow rate. The second set of regression equations describes relationships between the declination angle in kerf as the independent variable and either the Ra or the Rz parameters as dependent variables. The models can be used to predict cutting variables to predict the surface quality parameters. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Integration and Evaluation of Intra-Logistics Processes in Flexible Production Systems Based on OEE Metrics, with the Use of Computer Modelling and Simulation of AGVs

Krzysztof Foit

Grzegorz Gołda

and

Adrian Kampa

Processes 2020, 8(12), 1648; https://doi.org/10.3390/pr8121648 - 14 Dec 2020

Cited by 8 | Viewed by 2297

Abstract

The article presents the problems connected with the performance evaluation of a flexible production system in the context of designing and integrating production and logistics subsystems. The goal of the performed analysis was to determine the parameters that have the most significant influence on the productivity of the whole system. The possibilities of using automated machine tools, automatic transport vehicles, as well as automated storage systems were pointed out. Moreover, the exemplary models are described, and the framework of simulation research related to the conceptual design of new production systems are indicated. In order to evaluate the system’s productivity, the use of Overall Equipment Efficiency (OEE) metrics was proposed, which is typically used for stationary resources such as machines. This paper aims to prove the hypothesis that the OEE metric can also be used for transport facilities such as Automated Guided Vehicles (AGVs). The developed models include the parameters regarding availability and failure of AGVs as well as production efficiency and quality, which allows the more accurate mapping of manufacturing processes. As the result, the Overall Factory Efficiency (OFE) and Overall Transport Efficiency (OTE) metrics were obtained. The obtained outcomes can be directly related to similar production systems that belong to World Class Manufacturing (WCM) or World Class Logistics (WCL), leading to the in-depth planning of such systems and their further improvement in the context of the Industry 4.0. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Stability of Optimal Closed-Loop Cleaning Scheduling and Control with Application to Heat Exchanger Networks under Fouling

Federico Lozano Santamaria

and

Sandro Macchietto

Processes 2020, 8(12), 1623; https://doi.org/10.3390/pr8121623 - 09 Dec 2020

Cited by 1 | Viewed by 1679

Abstract

Heat exchanger networks subject to fouling are an important example of dynamic systems where performance deteriorates over time. To mitigate fouling and recover performance, cleanings of the exchangers are scheduled and control actions applied. Because of inaccuracy in the models, as well as uncertainty and variability in the operations, both schedule and controls often have to be revised to improve operations or just to ensure feasibility. A closed-loop nonlinear model predictive control (NMPC) approach had been previously developed to simultaneously optimize the cleaning schedule and the flow distribution for refinery preheat trains under fouling, considering their variability. However, the closed-loop scheduling stability of the scheme has not been analyzed. For practical closed-loop (online) scheduling applications, a balance is usually desired between reactivity (ensuring a rapid response to changes in conditions) and stability (avoiding too many large or frequent schedule changes). In this paper, metrics to quantify closed-loop scheduling stability (e.g., changes in task allocation or starting time) are developed and then included in the online optimization procedure. Three alternative formulations to directly include stability considerations in the closed-loop optimization are proposed and applied to two case studies, an illustrative one and an industrial one based on a refinery preheat train. Results demonstrate the applicability of the stability metrics developed and the ability of the closed-loop optimization to exploit trade-offs between stability and performance. For the heat exchanger networks under fouling considered, it is shown that the approach proposed can improve closed-loop schedule stability without significantly compromising the operating cost. The approach presented offers the blueprint for a more general application to closed-loop, model-based optimization of scheduling and control in other processes. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

An Agricultural Products Supply Chain Management to Optimize Resources and Carbon Emission Considering Variable Production Rate: Case of Nonperishable Corps

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Processes 2020, 8(11), 1505; https://doi.org/10.3390/pr8111505 - 20 Nov 2020

Cited by 8 | Viewed by 1824

Abstract

The management of the man–machine interaction is essential to achieve a competitive advantage among production firms and is more highlighted in the processing of agricultural products. The agricultural industry is underdeveloped and requires a transformation in technology. Advances in processing agricultural products (agri-product) are essential to achieve a smart production rate with good quality and to control waste. This research deals with modelling of a controllable production rate by a combination of the workforce and machines to minimize the total cost of production. The optimization of the carbon emission variable and management of the imperfection in processing makes the model eco-efficient. The perishability factor in the model is ignored due to the selection of a single sugar processing firm in the supply chain with a single vendor for the pragmatic application of the proposed research. A non-linear production model is developed to provide an economic benefit to the firms in terms of the minimum total cost with variable cycle time, workforce, machines, and plant production rate. A numerical experiment is performed by utilizing the data set of the agri-processing firm. A derivative free approach, i.e., algebraic approach, is utilized to find the best solution. The sensitivity analysis is performed to support the managers for the development of agricultural product supply chain management (Agri-SCM). Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperArticle

Material Requirements Planning Using Variable-Sized Bin-Packing Problem Formulation with Due Date and Grouping Constraints

Dejan Gradišar

and

Miha Glavan

Processes 2020, 8(10), 1246; https://doi.org/10.3390/pr8101246 - 02 Oct 2020

Cited by 2 | Viewed by 1726

Abstract

Correct planning is crucial for efficient production and best quality of products. The planning processes are commonly supported with computer solutions; however manual interactions are commonly needed, as sometimes the problems do not fit the general-purpose planning systems. The manual planning approach is time consuming and prone to errors. Solutions to automatize structured problems are needed. In this paper, we deal with material requirements planning for a specific problem, where a group of work orders for one product must be produced from the same batch of material. The presented problem is motivated by the steel-processing industry, where raw materials defined in a purchase order must be cut in order to satisfy the needs of the planned work order while also minimizing waste (leftover) and tardiness, if applicable. The specific requirements of the problem (i.e., restrictions of which work orders can be produced from a particular group of raw materials) does not fit the regular planning system used by the production company, therefore a case-specific solution was developed that can be generalized also to other similar cases. To solve this problem, we propose using the generalized bin-packing problem formulation which is described as an integer programming problem. An extension of the bin-packing problem formulation was developed based on: (i) variable bin sizes, (ii) consideration of time constraints and (iii) grouping of items/bins. The method presented in the article can be applied for small- to medium-sized problems as first verified by several examples of increasing complexity and later by an industrial case study. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessArticle

Process of Creating an Integrated Design and Manufacturing Environment as Part of the Structure of Industry 4.0

and

Processes 2020, 8(9), 1019; https://doi.org/10.3390/pr8091019 - 20 Aug 2020

Cited by 18 | Viewed by 2969

Abstract

This paper presents the process for creating an integrated design and manufacturing environment supporting 3D printing as part of the structure of Industry 4.0. This process is based on a developed framework for the design of modern automated and computerized infrastructure. The task of the described system is to combine all the steps included in the operating range of incremental systems based on an IT platform by integrating data from individual areas, such as IT systems supporting remote 3D printing. The proposed framework for incremental processes is a universal solution that can be defined in detail by a single organizational unit running 3D printing, as well as by a cluster of entities related to 3D printing. In the initial phase, the framework design includes a set of guidelines for IT (Information Technology) systems that facilitate the construction of individual elements and the creation of communication interfaces. In subsequent stages, the framework may already implement elements of the access and communication program interface, as well as guidelines for the industrial components to be included. The proposed framework for additive technologies is based on modern IT tools that enable the creation of geographically and functionally possible prototyping systems that can be integrated into the structure of Industry 4.0. To create optimal processes and economic systems, the principles of the construction and integration of individual services and equipment were developed. This new comprehensive approach is proposed in the present paper as a coherent framework. Moreover, the proposed solution has great potential for use in the design and production processes of various industries, such as chemicals, materials and construction. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessFeature PaperReview

Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

and

Marianthi Ierapetritou

Processes 2020, 8(9), 1088; https://doi.org/10.3390/pr8091088 - 02 Sep 2020

Cited by 64 | Viewed by 15122

Abstract

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessOpinion

Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution

Miriam Sarkis

Andrea Bernardi

Nilay Shah

and

Maria M. Papathanasiou

Processes 2021, 9(3), 457; https://doi.org/10.3390/pr9030457 - 03 Mar 2021

Cited by 17 | Viewed by 7534

Abstract

The rise of personalised and highly complex drug product profiles necessitates significant advancements in pharmaceutical manufacturing and distribution. Efforts to develop more agile, responsive, and reproducible manufacturing processes are being combined with the application of digital tools for seamless communication between process units, plants, and distribution nodes. In this paper, we discuss how novel therapeutics of high-specificity and sensitive nature are reshaping well-established paradigms in the pharmaceutical industry. We present an overview of recent research directions in pharmaceutical manufacturing and supply chain design and operations. We discuss topical challenges and opportunities related to small molecules and biologics, dividing the latter into patient- and non-specific. Lastly, we present the role of process systems engineering in generating decision-making tools to assist manufacturing and distribution strategies in the pharmaceutical sector and ultimately embrace the benefits of digitalised operations. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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Open AccessPerspective

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

and

Processes 2020, 8(12), 1641; https://doi.org/10.3390/pr8121641 - 12 Dec 2020

Cited by 13 | Viewed by 5942

Abstract

Continuous manufacturing of biologics (biopharmaceuticals) has been an area of active research and development for many reasons, ranging from the demand for operational streamlining to the requirement of achieving obvious economic benefits. At the same time, biopharma strives to develop systems and concepts that can operate at similar scales for clinical and commercial production—using flexible infrastructures, such as single-use flow paths and small surge vessels. These developments should simplify technology transfer, reduce footprint and capital investment, and will allow to react readily to changing market pressures while maintaining quality attributes. Despite a number of clearly identified benefits compared to traditional batch processes, continuous bioprocessing is still not widely adopted for commercial manufacturing. This paper details how industry-specific technological, organizational, economic, and regulatory barriers that exist in biopharmaceutical manufacturing are hindering the adoption of continuous production processes. Based on this understanding, the roles of process systems engineering (PSE), process analytical technologies, and process modeling and simulation are highlighted as key enabling tools in overcoming these multi-faceted barriers in today’s manufacturing environment. Of course, we do recognize that there is also a need for a clear set of regulations to guide a transition of biologics manufacturing towards continuous processing. Furthermore, the role played by the emerging fields of process integration and automation as well as digitalization is explored, as these are the tools of the future to facilitate this transition from batch to continuous production. Finally, an outlook focusing on technology, management, and regulatory aspects is presented to identify key concerted efforts required to drive the broad adaptation of continuous manufacturing in biopharmaceutical processes. Full article

(This article belongs to the Special Issue Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools)

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