Development Of Industrial Processes Research Articles

Integration and Evaluation of a Digital Support Function for Space Claims in Factory Layout Planning

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners’ knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

The Feasibility of Egypt's Photovoltaic Industry Drawing on China's Successful Experience

This study aims to evaluate the feasibility of Egypt's photovoltaic industry drawing on China's successful experience. Firstly, based on Egypt's national conditions and actual situation, the current situation, development potential, and challenges faced by the Egyptian photovoltaic market were analyzed.Egypts economic foundation remains less competitive, with outdated electricity infrastructure and long-term dependence on natural gas, posing challenges to its transformation. Egypt is one of the regions with the most concentrated solar energy resources in the world, which gives it the possibility and new opportunities to overtake in the photovoltaic field. Secondly, by analyzing the development process of China's photovoltaic industry, this paper explores China's successful experiences in policy support, market demand, technological level, financial support, and talent cultivation.Compared with China, Egypt's photovoltaic industry is still in an underdeveloped and developing stage, and requires sustained efforts in multiple aspects. This article compares the significant differences in the development of photovoltaic industries between China and Egypt, and analyzes the opportunities and challenges faced by Egypt's photovoltaic industry, as well as the corresponding strategies from three aspects: policy stability, demand activation, and desert photovoltaics. Finally, based on the research results, policy recommendations and development paths are proposed to promote the development of Egypt's photovoltaic industry, to provide reference and inspiration for the sustainable development of Egypt's photovoltaic industry.

A co-evolutionary approach to MNE-assisted industry development: Uruguay’s forest-based pulp sector

Purpose This study aims to investigate how co-evolutionary interactions between multinational enterprises (MNEs) and local actors shaped resource-intensive industry development over several decades, examining the economic, industrial and social implications of these interactions. It details the long-term effects of MNEs’ activities on the host country’s economic and social development and unfolds the path-dependent sequence and mechanisms of occurrence. Design/methodology/approach In a historical case study of Uruguay’s forest-based pulp sector during the 1970s to 2023, the authors analyze data from interviews with local industry actors and from company archives, academic publications and public environmental organizations. Findings The findings untangle the path-dependent co-evolution between MNEs’ activities and local actors and the resulting gradual development of the local industry. The increasing commitment of MNEs to the host country and their engagement with business and social communities impact the development of the industry’s technology basis, activity networks, identity and market. Spillovers and linkages occur in these interactions, driving industry development processes forward. The authors also reveal how MNEs attempt to address the social and environmental tensions associated with their operations to support their long-term presence in the host country. Research limitations/implications The authors contribute to the MNE-assisted development literature by untangling the specific mechanisms of MNEs’ engagement in local industry development over a long-term period and elaborating on its industrial and social implications. The authors enhance knowledge about spillovers by capturing a wide range of spillover and linkage effects and assessing their long-term impacts on industry development. Practical implications The authors offer insights into how policymakers can tailor instruments for attracting MNEs by adopting a long-term perspective regarding the implications for local development and accounting for economic, industrial and social impacts. This approach can maximize beneficial outcomes from MNEs’ presence and limit tensions between MNEs and local actors. Social implications This historical case study illustrates the complexity of MNEs’ engagement with local actors over time, including various tensions and positive developments. It illustrates the importance of social acceptance in achieving quality linkages between local and foreign actors. Originality/value This historical analysis untangles the path dependency in the long-term impacts of MNE-assisted development, illustrating the value of a temporal perspective.

The essence and assessment of modern industrial policy

The state of modern economy is exposed to significant challenges from geopolitical risks and transformation of the process of industrial development, which requires determining the conditions for implementing modern industrial policy and changing directions and priorities of industrial development. Improvement of the ways of production, development of the system of social institutions takes place when solving the issues related to the development of science underlying the formation of new industries. The subject of the study is the transformation of industrial policy features in modern economy at various levels of interaction between government and business. The article examines the changes in industrial development and corresponding transformation of industrial policy tools in import substitution development. The study aims at determining the relationships between the directions and levels of implementing industrial policy in accordance with system analysis and identifying the key features in the relationship between business structures and the state. The theories of industrial development and the concepts of resource and process approaches to industrial development and implementation of innovative industrial policy provide the methodological basis of the study. The study uses the methods of system analysis and synthesis in identifying the key relationships between business structures and the state in achieving the goals of industrial development. The study defines the transformation of types, tools and system of indicators of industrial policy under the sanction pressure on the economy, identifies the possibilities of using the matrix system in managing industrial development, implemented at three levels (macro-, meso- and microeconomic) in interaction of business structures and public authorities, assesses the possibility of forming and conducting conservative industrial policy in the lack of scientific research funding. The findings can be used in assessing the priorities, directions and transformation of the tools used to implement an innovative model of industrial policy in the Russian economy, in the development of import substitution and formation of new industries, to form an effective interaction of innovation process and the process of industrial development of the national economy.

Empirical Analysis on the Mechanism of Industrial Park Driving Urban Expansion: A Case Study of Xining City

Taking Xining City as an example, this article analyzes the mechanism by which industrial park construction drives the expansion of urban population size and built-up area, based on a review of the process of industrial park development and urban population growth. It also discusses future urban governance models in light of urban development trends. The research finds: (1) In the process of urban development, industrial park construction is often the initial factor in the cumulative and cyclical development of a city; (2) As the level of development improves and the mode of economic growth changes, the government should timely adjust its strategies, shifting from the expansion of industrial park construction towards structural optimization and quality improvement. The most significant difference from previous research is that this paper emphasizes the importance of government planning. This study can not only demonstrate the general process of industrial parks promoting urban expansion, but more importantly, it explains the fundamental reasons for the transition of urban expansion to adjustment from a mechanism perspective, thereby eliminating the drawbacks of simply predicting urban scale evolution through data models.

Offshore Wind Power Growth and Industrial Development in Emerging Markets

Offshore wind has developed significantly over the past decade, and promising new markets are emerging, such as Brazil, South Africa, India, Poland, and Turkey. As logistic transport activities increase complexities, developing regional supply chains can help to reduce costs and enhance the sector’s competitiveness. This article proposes a framework for the industrial development of the offshore wind supply chain in new markets. This study is grounded in a systematic literature review and is validated through a multi-case study, identifying key variables and factors influencing industrial growth. Adopting a process-based approach, factors and variables were modeled into a framework, encompassing the following four phases: (1) demand assessment of a new sector, (2) sectorial and industrial planning, (3) industrial development and maturity, and (4) sectorial and industrial renewal or decline. Each phase brings together a group of policies. Our findings show the policies’ interrelations. These results complement the few studies that have examined the industrial development process, providing a clear guide as to the process for the development of the offshore wind industry in specific regions. Thus, the framework provides elements that contribute as a valuable tool to the debate, structuring, and development of public policies for the industrial development of a new sector.

Fit models’ roles in identifying fit issues in the apparel technical design process and implications for improving 3D virtual fitting

Fit sessions are essential in making well-fitted clothing. During these sessions, apparel fit is determined iteratively by a team of designers, technical designers, fit models, and merchandisers. With the advent of the digitalization of new product development processes in the fashion industry, fit sessions have been seen as a bottleneck for they are still held in person. However, the process is currently still irreplaceable, and what makes the feedback provided by fit models important is an area that has never been tapped. Therefore, the present study aimed to understand fit models’ role in fit sessions as well as how they assess the fit of garments and deliver their feedback. On-site observation during fit sessions and individual interviews with fit models were conducted. It was found that fit models gave comfort, fit, and tactile comments by testing garments while standing and moving between several postures. They had knowledge that overlapped with those of technical designers and designers and this empowerment enabled them to take part in the decision-making process in fit sessions. It also was found that fit models’ feedback on garments was essential as they were the first people to try on the garments and present the customers’ points of view.

An improved industrial fault diagnosis model by integrating enhanced variational mode decomposition with sparse process monitoring method

With the continuous development of intelligent industrial processes, the sparse principal component analysis (SPCA), as a promising process monitoring method, has been widely used in the field of industrial fault detection. However, due to the inadequacy of data preprocessing and the insufficient detection accuracy for minor faults, the SPCA models exhibit obvious limitations in dealing with the processes with dynamic and temporal features. In this study, a Harris Hawk optimization method enhanced variational mode decomposition (HHO-VMD) coupled with the sliding window optimized adaptive SPCA (SWOASPCA) method is proposed to improve the fault detection performance of the SPCA models. In the HHO-VMD-SWOASPCA method, the process data is first preprocessed by adaptively and iteratively optimizing the number of modes and penalty terms in the VMD method via the Harris Hawk Optimization (HHO) method, and then the original SPCA model is combined with the sliding window method and the weight assignment strategy to enhance the model's adaptive capability and accuracy to detect minor faults. Moreover, an improved reconstruction-based contribution (RBC) method is presented to diagnose the detected faults for determining the fault causes. The effectiveness of the proposed method is verified by its application in the industrial sugar production process.

Acid Strength Effects on Dimerization during Metal-Free Catalytic Dioxygen Reduction.

Development of earth-abundant catalysts for the reduction of dioxygen (ORR) is essential for the development of alternative industrial processes and energy sources. Here, we report a transition metal-free dicationic organocatalyst (Ph2Phen2+) for the ORR. The ORR performance of this compound was studied in acetonitrile solution under both electrochemical conditions and spectrochemical conditions, using halogenated acetic acid derivatives spanning a pKa range of 12.65 to 20.3. Interestingly, it was found that under electrochemical conditions, a kinetically relevant peroxo dimer species forms with all acids. However, under spectrochemical conditions, strong acids diminish the kinetic contribution of this dimer to the observed rate due to lower catalyst concentrations, whereas weaker acids were still rate-limited by the dimer equilibrium. Together, these results provide insight into the mechanisms of ORR by organic-based, metal-free catalysts, suggesting that balancing redox activity and unsaturated character of these molecules with respect to the pKa of intermediates can enable reaction tuning analogous to transition metal-based systems.

A Dye-Sensitized Sensor for Oxygen Detection under Visible Light.

Sensors that can accurately assess oxygen (O2) concentrations in real time are crucial for a wide range of applications spanning personal health monitoring, environmental protection, and industrial process development. Here a high-performance chemiresistive sensor that allows for the rapid detection of O2 at room temperature under visible light illumination is described. Inspired by the operating principles of dye-sensitized solar cells, the chemiresistor is based on a single-walled carbon nanotube-titania hybrid (SWCNT-TiO2) bearing a molecular Re-based photosensitizer [(Pbpy)(CO)3ReBr] (Pbpy = 4,4'-[P(O)(OH)2]2-2,2'-bipyridine). The resulting SWCNT-TiO2-Re composite undergoes photoinduced charge transfer that is sensitive to ppb levels of O2, thereby yielding a rapid and reversible chemiresistive response. Owing to its unique mode of operation and robust components, the sensor shows a high degree of selectivity for O2 over a range of interferants, humidity tolerance, and multimonth benchtop stability. The approach presented herein demonstrates the translatability of concepts in light harvesting to the development of robust, rapid, and low-power sensing technologies.

Steric Effects of Tetraalkylammonium Hydroxides on Nafion in Adiponitrile Production

Over the last two decades, the chemical industry has witnessed a 43% increase in greenhouse gas emissions, with organic chemicals contributing to 75% of the total chemical production [1]. While organic electrosynthesis is a promising method for reducing the environmental impact of chemical manufacturing, advancing this technology faces significant challenges due to the limited viability of membranes used in divided electrochemical reactors. Despite the need for suitable membranes for electrosynthesis, only a few studies have explored the use of ion-conducting membranes for electro-organic reactions [2-7]. In this study, we aim to provide additional insights into the role of membranes in electrosynthesis and their behavior when exposed to electrolytes containing organic components.We selected adiponitrile electrosynthesis, one of the largest industrial electro-organic processes, as a model reaction [2,3]. We used Nafion as a model membrane material. Electrolytes used in adiponitrile electrosynthesis contain tetraalkylammonium (TAA) ions to enhance the selectivity of electro-organic reactions. Here, we present a systematic study exploring how TAA ions impact Nafion's structure and transport properties. We exposed Nafion membranes to solutions with varying concentrations and sizes of TAA ions, including tetramethyl (TMA), tetraethyl (TEA), tetrapropyl (TPA), and tetrabutyl (TBA). We quantified TAA ion sorption in the membranes using ATR-FTIR. The results reveal that TAA ion sorption saturates when the solution concentration exceeds 0.02 M. Compared to the conductivity of Nafion equilibrated in pure water (9.8 x 10-2 S/cm), the membrane conductivity after exposure to TAA solutions exhibits a substantial decline, decreasing by up to 4 orders of magnitude. We performed small-angle X-ray scattering experiments to correlate the conductivity results with structural changes in Nafion. The measurements revealed that the spacing between Nafion's ion-conducting domains decreases as the TAA ion concentration increases. For example, when membranes are exposed to 1.0 M TBA solutions, the domain spacing drops from 4.9 nm to 3.76 nm compared to membranes equilibrated in pure water. Consequently, we infer those organic ions reduce the size of ion-conducting domains due to the diffusion of water from the membranes into the surrounding electrolyte, resulting in decreased conductivity. These results represent an important step in elucidating structure-property relationships in ion-conducting membranes used for organic electrosynthesis.Reference[1] 2022. Our Risks for Infectious Diseases Is Increasing Because of Climate Change. CDC:NCEZID [updated 2022 August 02]. https://www.cdc.gov/ncezid/what-we-do/climate-change-and-infectious-diseases/index.html.[2] N. Tanbouza. 2020 J. iScience. 23 101720[3] Suryanto, B. H. R.; Kristianto, H. A.; Yi, Z. "Electrosynthesis: An overview of green chemistry.” Current Opinion in Green and Sustainable Chemistry 16 (2019): 28-34.[4] Baizer, M.; Lust, E.; Williamson, S.E.; Connor, R.F. "Electrochemical Synthesis of Adiponitrile." Industrial & Engineering Chemistry Process Design and Development, 1966, 5 (1), 56–62.[5] D. E. Danly 1984 J. Electrochem. Soc. 131 435C[6] Blanco, D.; Prasad, P.; Dunningan, K.; Modestino, M. React. Chem. Eng., 2020, 5, 136[7] Katzenberg, A.; Angulo, A.; Kusoglu, A.; Modestino, M. Macromolecules 2021, 54, 5187−5195 Figure 1

Application of Deep Learning for Automatic Identification of Hazardous Materials and Urban Safety Supervision

The rapid process of urbanization and industrial development has raised significant concerns regarding the presence and management of hazardous substances. However, conventional methods employed for identifying hazardous substances and monitoring urban safety often suffer from low efficiency and accuracy. This paper proposes a novel approach that combines deep learning and genetic algorithms, which utilizes the Bidirectional Long Short-Term Memory model to capture temporal features in hazardous substance data and introduces the Attention Mechanism for weighted processing of crucial information, thereby improving recognition capability. Genetic Algorithms are employed to optimize the performance and generalization capacity of the deep learning model. Experimental validation demonstrates that the proposed approach achieves higher accuracy and faster processing speed, effectively enhancing urban safety monitoring. This research holds practical implications for urban safety management and accident prevention, offering an innovative solution to guarantee urban safety.

Development and Control of Virtual Industrial Process using Factory IO and MATLAB

In today's rapidly evolving business landscape, the strategic adoption of virtual manufacturing methods has emerged as a key driver for companies seeking to streamline operations and expedite product launches in a cost-effective manner. This progressive approach involves the creation of a synthetic and interconnected environment, empowered by advanced software tools and systems, including Virtual Reality and Simulation technologies, tailored to optimize industrial processes. Our methodology employs a unique combination of two simulation software tools: Factory I/O for process development and MATLAB for control program implementation. Furthermore, we explore the use of the Modbus TCP/IP communication protocol as the framework for seamless interaction between these software tools during simulation. This research presents practical insights into the transformative potential of virtual manufacturing, showcasing its real-world application in enhancing operational efficiency and agility within industrial settings.

Deep learning with local spatiotemporal structure preserving for soft sensor development of complex industrial processes

Data-driven soft sensors have emerged as indispensable tools for predicting quality variables in complex industrial processes because of their cost-effectiveness and ease of maintenance. In particular, soft sensors based on deep learning have been utilized in extensive research and successful applications in recent years. However, traditional deep learning methods capture hierarchical data features by minimizing global fitting errors, neglecting the local structural characteristics implied in the original data. In this paper, we propose a new deep learning method for soft sensor development. Utilizing autoencoders as the foundational architecture of our network, a new semisupervised strategy is adopted for layerwise pretraining optimization. On the one hand, more representative data features are extracted by maintaining the local spatiotemporal structure of the data; on the other hand, layer-by-layer supervised learning is employed to identify the critical features that are aligned with the ultimate task, which aids in obtaining the optimal network parameters and improving the resulting prediction accuracy. Subsequently, a local spatiotemporal structure-preserving stacked semisupervised autoencoder (LSP-SuAE) is established. To evaluate the feasibility and effectiveness of the proposed approach, experiments are carried out in a real industrial process. A soft sensor based on the LSP-SuAE is developed to predict the rate of ethylbenzene conversion during the dehydrogenation of styrene. The experimental results demonstrate that, compared to five other common or similar data-driven modeling methods, LSP-SuAE exhibits higher prediction accuracy and better stability.

Phosphorus recovery and tetracycline Mitigation: The role of Bacillus cereus LB-9 in struvite biomineralization from wastewater

Struvite biomineralization is an ecologically sound technology, adept at the efficient recovery and recycling of phosphorus from wastewater. However, the biomineralization process is often perturbed by the presence of antibiotics, notably tetracycline (TC), the impact of which on the biomineralization system has not been elucidated. This study examines the efficacy of Bacillus cereus LB-9 in struvite biomineralization, focusing on the precipitates' composition, morphology, and TC content. LB-9 facilitate an alkaline environment that effectively recovering nitrogen and phosphorus. These findings indicate that TC retards the initial formation of struvite and the concurrent recovery of nitrogen and phosphorus. However, at concentrations below 10 mg/L TC concentrations, TC enhanced struvite production (0.38g) by stimulating LB-9's growth and metabolic activity. Conversely, at a concentration of 10 mg/L TC, the strain's activity was markedly suppressed within the initial four days. This data suggests that TC promotes the strain's proliferation and metabolism, potentially through cellular secretions, thereby augmenting phosphorus recovery from wastewater. Notably, the recovered struvite doesn't contain TC, aligning with regulatory standards for agricultural application. In summary, LB-9-mediated struvite recovery is an effective strategy for producing phosphorus-enriched fertilizers and mitigating TC contamination, offering significant implications for wastewater treatment and industrial process development, particularly in the context of prevalent TC in wastewater.

Eco-Friendly Isolated Nanocellulose from Seaweed Biomass via Modified-Acid and Electron Beam Process for Biodegradable Polymer Composites

Nanocellulose (NC) has emerged as a promising biodegradable material with applications in various industrial fields owing to its high mechanical strength, thermal stability, and eco-friendly properties. Traditional methods for isolating NC from wood-based biomass (WB) involve high energy consumption and extensive chemical usage, leading to environmental and sustainability concerns. This study explored an alternative approach to isolate NC from seaweed-based biomass (SB) (SNC), which contains fewer non-cellulosic components and a higher cellulose content than WB, thereby yielding a more efficient e-isolation process. We employed a combination of modified-acid solution and electron beam (E-beam) technology to isolate NC from SB. The E-beam process enhanced the crystallinity while reducing the particle size, thus facilitating NC isolation with reduced environmental impact and processing time. Moreover, our method significantly reduced the need for harsh chemical reagents and energy-intensive processes, which are typically associated with traditional NC isolation methods. We fabricated biodegradable films with improved mechanical properties using NC as a reinforcing agent in polymer composites, thereby demonstrating the potential of NC-based materials for various applications. Therefore, our proposed approach offers a sustainable and efficient method for NC isolation and serves as a guide for the development of eco-friendly industrial processes. Our findings contribute to ongoing efforts to create sustainable materials and reduce the environmental footprint of the manufacturing industry.

Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes

Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes

Dyspersja metanu w zamkniętych obudowach

The increase of the population also involve the increase of the consumption of raw materials. This requires the diversification and development of industrial processes in semi-enclosed or open spaces. The carrying out of human activities of an industrial nature involves the accidental use, handling or presence of explosive substances such as methane. The presence of this gas in closed or semi-closed spaces can generate explosion phenomena. The accumulation of methane in narrow spaces is well studied and known, but the dispersion and especially the dispersion dynamics of methane released from a source considered infinite is less known. Knowing how methane disperses into the air is very important for establishing preventive measures. The paper presents the experiment on the dynamics of methane dispersion in a closed enclosure.

Research on the Development and Policy Evolution of CCUS Industry at Home and Abroad

CCUS is internationally recognized as one of the three major pathways to achieve carbon neutrality goals. It is an important choice for realizing zero emissions from large-scale fossil energy utilization and a feasible technological solution to offset carbon emissions in industries such as power, steel, and cement where emissions reductions are challenging. Systematic analysis of the development and policy evolution of the CCUS industry at home and abroad can provide theoretical basis and practical guidance for China's energy transition and development under the background of carbon emission peaking and achieving carbon neutrality. Guided by the strategic goals of national energy security, carbon emission peaking, and achieving carbon neutrality, this study aims to analyze the global development process and stage characteristics of the CCUS industry, investigate the supporting policies in the CCUS field and their evolution patterns, summarize the current status and trends of the CCUS industry at home and abroad, and provide reference for the implementation of national energy green and low-carbon transformation and the construction of a new energy system. In terms of industry, European and American countries emphasize national-level technological guidance and macro-control. The United States has introduced the progressive 45Q tax credit policy, and the European Union has included CCUS in its carbon trading system. The US National Carbon Capture Center provides a testing environment and facilities for CCUS technology research and development, and has established a carbon dioxide industry cluster and transportation hub. In terms of policies, tax credits and carbon trading policies in Europe and America have attracted various types of capital investment, establishing a relatively complete legal framework system. These regions have been leading in CCUS technology research and deployment, holding dominant positions and decision-making power in mainstream international CCUS organizations such as the CSLF, IEA, GCCSI, and OGCI. This study benchmarks the forward-looking and strategic development status of the CCUS industry at home and abroad, elucidates the challenges facing CCUS industry development, and proposes future trends and policy support needs for the CCUS industry. The research reveals that Europe and America emphasize national-level technological guidance and macro-control, focusing on the construction of regional industrial networks and having established a relatively complete legal and regulatory framework system. Compared to other countries, China needs to establish national-level guidance on CCUS development, regional carbon dioxide capture and transportation networks, and enact specific laws, regulations, and technical standards for CCUS.