Industrial Production Systems Research Articles

Photovoltaic and Solar-Thermal Use Case Application Comparison with Witness Simulation and DCF Analysis

In this work, we investigate a system of solar and thermal heat collectors in combination with local and electric net storage. The system is implemented in a Witness Horizon simulation model, allowing for the investigation and scenario analysis of different use cases. In a practical industrial application example use-case, using simulation and Discounted Cash Flow (DCF) analysis, the economic feasibility of the best system variant could be calculated. The developed model includes the local consumption and production profile and it was evaluated by a DCF model after the dynamic energy modelisation with the generic industrial production system simulation software WITNESS Horizon. This model can be in future further extended to generic and more complex production systems, simultaneously integrating the DCF cost evaluation analysis and following direct economic optimisation as well as integrating it with the production portfolio theory approach by integrating diversified virtual production market aspects and market goal-oriented optimisation.

A large-scale graph clustering method for cell conditions spatio-temporal localization in aluminum electrolysis

Sensor nodes (industrial nodes) clustering is an efficacious method for gaining local working conditions real-timely in the production process. The existing deep graph neural networks-based industrial nodes clustering studies construct static graphs to describe the potential spatial correlation among industrial nodes, but the temporal correlation among industrial nodes is ignored, resulting in inferior real-time performance. Meanwhile, these deep learning models fail to obtain good clustering results due to the large-scale and noise-containing characteristics of industrial nodes. In this paper, a large-scale graph clustering method (LsGCM) is proposed for cell conditions spatio-temporal localization in aluminum electrolysis to address the above issues. Specifically, a spatio-temporal graph construction method based on industrial process mechanism knowledge and local working condition boundary types statistical feature fusion is proposed for capturing effective spatio-temporal correlations between industrial nodes. Then, hysteresis knowledge of industrial production system is used to guide the nodes merging, thereby avoiding the incorrect merging of time-asynchronous nodes when performing the spatio-temporal region detection of local working conditions. Finally, combined with the actual spatio-temporal location information of industrial nodes, a novel spatio-temporal information expansion method (StIEM) of the sub-spatio-temporal graph describing local working condition is proposed for obtaining spatio-temporal feature information of the neighborhood, which is beneficial to improve the performance of industrial node clustering. Validation studies on a numerical simulation example and multiple real aluminum electrolysis industrial datasets certificate the effectiveness and superiority of the proposed method. In particular, compared with existing graph clustering methods, our method improves the clustering accuracy by 7.79%.

Analysis of parameters for spray-local exhaust ventilation (SLEV) to minimize high-temperature smoke pollutants and reduce energy consumption

Industrial buildings often face limitations in placing local exhaust ventilation (LEV) near pollution sources, which can lead to ineffective control of high-temperature smoke. This study explores the integration of spray with local exhaust ventilation (SLEV) as a solution for smoke control and energy savings. The research identified a design law for spray parameters and developed a formula to calculate SLEV ventilation flow rate that considers the impact of spray. Results indicate that the spray field overlapping coefficient plays a key role in characterizing the position and spacing of multi-nozzles. As the spray field overlapping coefficient increases, the spray dust reduction performance indicator linearly increases, while the capture efficiency follows a pattern that aligns with the Boltzmann curve model. The optimal range for the spray field overlapping coefficient to achieve high efficiency in SLEV is between 0.45 and 0.55. The formula for ventilation flow rate calculation is applicable to common conditions with smoke velocities ranging from 4 to 12 m/s (Ar: 0.0149–0.1339). SLEVs demonstrate a 35.22 % improvement in dust removal efficiency and over 20 % energy savings compared to LEVs, contributing to the development of high-efficiency, energy-saving systems for sustainable industrial production.

An integrated system theoretic process analysis with multilevel flow modeling for the identification of cyber‐physical hazards in a process industry

AbstractThe deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the “5 flows” concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems.

Application of bluetooth mesh network in multi-device system management

Currently, the application of the Internet of Things (IoT) in the industrial sector to improve the productivity and efficiency of industrial processes is no longer unfamiliar. However, for a wireless control system to operate reliably and stably even without the Internet, additional issues regarding energy and alternative control options need to be addressed. In this article, the authors have investigated the application of Bluetooth Mesh networks in industrial production systems. In a manufacturing facility, Bluetooth devices are installed on machinery and can exchange data with each other through a gateway device that serves as a data receiver for the Bluetooth devices. Additionally, the system can be remotely monitored and controlled through a Web server. Implementing this system in production enables industrial companies to digitize processes, transform business models, and enhance performance and productivity.

Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems

The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning.

Discuss on the Construction of Digital Rural Revitalization Information Service Platform in the New Era

If we want to assume digital rural areas, the focus must be on industrial prosperity, adhere to quality first, green agriculture, and take the structural reform of the agricultural supply side as a guide to further promote the construction speed of modern agricultural industrial system, management system, and production system, thereby improving agricultural productivity, competitiveness, and innovation, and promoting the realization of an agricultural power. In the new era, the system engineering of digital rural projects is relatively complex. It is necessary to adopt advanced system engineering ideas and methods, arrange top-level design, institutional setting, innovative technology, talent cultivation, and optimize operation modes, in order to achieve an information service platform and achieve digital rural revitalization.

Problems and prospects for the development and application of an algorithm for the transition to industrial digitalization

Industrial digitalization defines global prospects for increasing the efficiency of industrial production systems. Issues of industrial digitalization are accompanied by a number of complex transformation processes and preparatory procedures, the provision of which requires an integrated approach and the development of special algorithms, strategies and supporting mechanisms for industrial digitalization. The article analyzes modern problems and prospects for the development and implementation of an algorithm for the transition of enterprises to industrial digitalization. General factors are identified: limitations of industrial digitalization and prospects for transformation. A generalized algorithm is developed and the mechanisms and approaches to industrial digitalization at the micro level that support its implementation are revealed.

Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control

A fuzzy RBF-PID control strategy based on particle swarm optimization (PSO) algorithm is proposed to solve the problem of large inertia lag in temperature control system of industrial production refuse furnace. In this control system, an improved particle swarm optimization algorithm combined with inertia weight and genetic transformation was used to optimize the initial values of membership functions of fuzzy RBF (radial basis function). Then, BP (error backpropagation) algorithm is used for fine tuning, and fuzzy reasoning and RBF learning ability are combined to adjust the PID control parameters online to achieve the optimal PID control effect. The simulation results show that the algorithm has fast tracking, small overshoot, and is not easily trapped in local minima. At the same time, its robustness and anti-interference performance are better than traditional PID control.

Experimental assessment and prediction of design parameter influences on a specific vacuum-based granular gripper

Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as in their configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This influences the achievable gripping forces, which vary with the gripped objects geometry. On the basis of experimental studies, the modeling of interpolations as well as through experimental validations, the present research investigates the influences of different configurations on the achievable gripping forces for a specific concept of an innovative vacuum-based granular gripper. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling the effects of their interrelations on the achievable gripping force is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper.

Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production

AbstractTo achieve a highly agile and flexible production, a transformational shift is envisioned whereby industrial production systems evolve to be more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, information about individual production assets is accessible throughout their entire life-cycles. To realize this vision, the use of advanced information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs are generally more applied to monitor production resources and process information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.

A systematic review of the definitions, narratives and paths forwards for a protein transition in high-income countries

The protein transition, aiming to rebalance protein intake between animal and alternative proteins, is gaining momentum in scientific and policy discussions on food system transformation. Here, using a systematic review approach, we identified 33 articles that address challenges in reducing the environmental impacts of protein production and consumption, providing healthy diets for a growing population and preventing adverse effects of industrial livestock production systems. We found unclear definitions of the protein transition, conflicting views on reduction or replacement of dietary protein and a lack of attention to systemic change by reducing protein to its macronutrient function. Three narratives were identified, namely, the consumer narrative focusing on consumption-based solutions targeting dietary changes; the techno-centred narrative developing new, more resource-efficient protein production systems; and the socio-technological narrative that intends to transition the agri-food system from an animal-dominated regime to an alternative protein regime. We conclude that solutions should consider factors such as scale, initiating actors and expected impact to support complementary protein transition approaches.

Unsupervised Correlation- and Interaction-Aware Anomaly Detection for Cyber-Physical Production Systems based on Graph Neural Networks

Anomaly detection is essential for realising state-of-the-art and safe cyber-physical production systems. With the early detection of possible anomalies, the rise and the carryover of a failure throughout the entire manufacture can be prevented. Current anomaly detection approaches use sensor data from the live system only. Therefore, many important characteristics, interactions, and correlations within the system are neglected. Graphs open a particular way of modelling cyber-physical production systems, while at the same time, they allow the inclusion of a wider variation and variety of data about the system. In this study, we introduce an unsupervised correlation- and interaction-aware anomaly detection concept for cyber-physical production systems based on graph neural networks. We define a concept that allows modelling the entire system as a graph, including external knowledge. We introduce a reconstruction-based graph neural network to detect and analyse the anomalies in these graph structures. The concept is evaluated in a real industrial cyber-physical production system. The test results confirm that the presented concept can be applied to detect anomalies while concurrently considering the correlations and interactions of the system.

Internet of Production: Unleashing the Full Potential of Industry 4.0 – A Comprehensive Review of Trends, Drivers, and Challenges

The rapid advancement of Industry 4.0 technologies has revolutionized industrial processes and production systems, creating unprecedented optimization opportunities. The Internet of Production (IoP) concept has emerged as a transformative approach that capitalizes on integrating cyber-physical systems, big data analytics, artificial intelligence, and the Industrial Internet of Things (IIoT) to drive efficient, flexible, and intelligent manufacturing. This study explores the current state of expert discourse on the symbiotic relationship between IoP and Industry 4.0, focusing on production technology, automation, and digitalization. For this purpose, we performed a content-centric qualitative review in a systematic manner. The findings show that connectivity, data-driven decision-making, flexibility, intelligent optimization, digital twins, and collaborative networks promotes IoP's potential within Industry 4.0. Overall, this study explains how harnessing the potential of IoP by enhancing efficiency, productivity, and innovation in industrial processes contributes to Industry 4.0, and how understanding the strengths, weaknesses, opportunities, and threats of IoP, leveraging advanced technologies, and data-driven approaches can pave the way for the future of efficient, flexible, and intelligent manufacturing systems.

Evolutions of sustainable supply chain management in emerging economies: A literature review

The escalating demand for items and their consumption has exerted immense pressure on industrial production and supply chain systems, leading to adverse environmental and societal consequences. The rise in pollution rates and ecological disasters resulting from industrial production has prompted numerous scholars and industry specialists to focus on Sustainable Production and Consumption matters within the framework of Sustainable Supply Chain Management (SSCM). The interest in sustainable supply chain management has significantly increased in recent years, spanning both business and academic sectors. This phenomenon is seen in the growing prevalence of papers, conferences, specialized periodicals, and websites exclusively focused on the subject matter. Nevertheless, the importance of sustainable development in developing economies has only just started to be acknowledged. This literature review aims to assess existing research on sustainable supply chain management (SSCM) in developing nations while considering a worldwide perspective. This paper thoroughly examines the rapid expansion of the subject from an evolutionary perspective; aimed at comprehending the progression of sustainability concerns by examining patterns across different industries, and economies, and employing diverse approaches. An extensive thematic analysis was conducted on 56 selected publications published between 2010 and 2023, emphasizing the growth and significance of the knowledge base. The analysis is conducted utilizing a descriptive and content-oriented methodology. Subsequently, the results are presented, demonstrating an increasing interest in Sustainable Supply Chain Management (SSCM). Nevertheless, there is a conspicuous discrepancy in the extent of research carried out on this subject in emerging economies as opposed to industrialized ones. The findings indicate that the context in less developed countries plays a crucial role in carrying out empirical or case study research. Moreover, it is crucial to analyze how the integration of the three dimensions of sustainability impacts the efficiency of the supply chain, especially when considering the perspective of an emerging economy. Hence, the limitations of this investigation are delineated. Ultimately, it is crucial to do further research from multiple angles within the supply chain, encompassing collaboration, sustainable practices, innovation, sourcing, and supplier growth, with a special emphasis on the position and background of rising countries.

ПРЕДПОСЫЛКИ РАЗВИТИЯ ИНДУСТРИАЛЬНЫХ МЕТОДОВ СТРОИТЕЛЬСТВА В САНКТ-ПЕТЕРБУРГЕ

The article considers the technologies for the construction of mainly civil buildings in different periods of construction in St. Petersburg. The subject of the study is building systems and building construction technologies, which are considered in order to study the development of industrial construction methods in St. Petersburg and predict trends in technical progress in the field of industrial construction methods. The prerequisites for technological progress in the construction complex of St. Petersburg is the development of the industry of material and technical resources of construction, namely the production of new building materials, structures, construction equipment, fixtures and construction machines, as well as small-scale mechanization and tools. This inevitably becomes the reason for the appearance in construction of more advanced building systems erected by industrial methods. In the course of the study, building systems were evaluated by the methods of an expert survey of specialists according to a previously developed questionnaire, in which there were mutually incomparable criteria reflecting the architectural, strength and economic characteristics of building systems of industrial production. The results of an expert assessment of building systems became the basis for determining the rational scope of prefabricated and precast-monolithic construction and identifying trends and promising directions for the development of industrial construction methods and scientific research in this area.

Modeling and optimization of the hybrid flow shop scheduling problem with sequence-dependent setup times

The hybrid flow shop scheduling problem (HFSP) is an extension of the classic flow shop scheduling problem and widely exists in real industrial production systems. In real production, sequence-dependent setup times (SDST) are very important and cannot be neglected. Therefore, this study focuses HFSP with SDST (HFSP-SDST) to minimize the makespan. To solve this problem, a mixed-integer linear programming (MILP) model to obtain the optimal solutions for small-scale instances is proposed. Given the NP-hard characteristics of HFSP-SDST, an improved artificial bee colony (IABC) algorithm is developed to efficiently solve large-sized instances. In IABC, permutation encoding is used and a hybrid representation that combines forward decoding and backward decoding methods is designed. To search for the solution space that is not included in the encoding and decoding, a problem-specific local search strategy is developed to enlarge the solution space. Experiments are conducted to evaluate the effectiveness of the MILP model and IABC. The results indicate that the proposed MILP model can find the optimal solutions for small-scale instances. The proposed IABC performs much better than the existing algorithms and improves 61 current best solutions of benchmark instances.

Comparative analysis of production systems in the automotive industry

Purpose of the following text is to compare three production systems,: Toyota Production System, World Class Manufacturing and Nissan Production Way. For it purpose, a comparative study was carried out based on three categories: system, design, popularity and versatility. For each of the comparison categories, a method was adjusted. System design: comparison of assumptions and system construction schemes according to the authors of a given solution. Popularity: analysis of search engine results from Google, Google Scholar, Bing and Yahoo! obtained from searches for the phrases "Toyota Production System," "World Class Manufacturing" and "Nissan Production Way". Versality: case studies of system implementations by other organisations than authoring automotive corporations. As a result of the comparison, it can be seen that the most complex and elaborate system is WCM, which has a multi-element structure as well as predetermined levels of sophistication. Relatively the simplest of the compared solutions turned out to be NPW. In the case of popularity, the leader is TPS in Google’s search engines, WCM in Bing and Yahoo!, while the least popular solution in the Internet’s space is the system by Nissan Motor Corporation. The versatility category indicates that the TPS and WCM systems have similar implementation capabilities, while NPW turns out to be a specific and exclusive solution for the Renault-Nissan-Mitsubishi Alliance and has no external implementations. The results provide an overall picture of the situation regarding production systems in the automotive industry and can provide a starting point for more advanced analysis of existing solutions and allow the design of other systems.

Multiresolution analysis for tool failure detection in CFRP/Ti6Al4V hybrid stacks drilling in aircraft assembly lines

This article presents a method for identifying Catastrophic Tool Failures during CFRP/Ti6Al4V hybrid stacks drilling in aircraft fuselage components assembly. The study is based on a real industrial case in the production system of a commercial aircraft section, where data was collected from the aircraft assembly line for 17 months. Over 30 different tool configurations were utilized in machining the aircraft section. However, the tool exhibiting the highest breakage rate, specifically a 4.6 mm diameter spiral fluted drill designed for CFRP/Ti6Al4V applications, was chosen for further analysis. This data collection included 69 samples of the selected cutting tool, revealing 19 instances of tool failure. The proposed detection method for spindle consumption signals utilizes Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT) and Z-score technique. The research provides a comprehensive analysis of the use of DWT, explaining how the approximation and detail coefficients are obtained, which padding mode is suitable for the industrial case, the influence of the sliding process and filter bank length on the detection of sudden drops in the signal, and how to choose a suitable decomposition level in MRA based on the information provided by the scalograms. The confusion matrix results demonstrate the method's high performance, with Recall and Precision values equal to 1, which means that 100 % of tool failures were detected with no false positives. Additionally, the algorithm running time is 0.65 s, indicating that the method would apply to real-time tool failure detection in industrial machining production systems.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.