Article
Business
Ghulam Qader, Muhammad Junaid, Qamar Abbas, Muhammad Shujaat Mubarik
Summary: This study investigates the impact of Industry 4.0 on supply chain performance, with the mediating role of supply chain resilience and the moderating role of supply chain visibility. The findings suggest that Industry 4.0 has a significant impact on supply chain performance and that supply chain resilience and visibility play important roles in this relationship.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Computer Science, Interdisciplinary Applications
Sjoerd Rongen, Nikoletta Nikolova, Mark van der Pas
Summary: Industry 4.0 introduces the Asset Administration Shell (AAS) model for digital twins to address interoperability issues, which can also be tackled by the Semantic Web and its Resource Description Framework (RDF). Both AAS and RDF-based models have their own strengths, with AAS models being easier to integrate with operational technologies and RDF-based models offering more semantic expressiveness and advanced querying.
COMPUTERS IN INDUSTRY
(2023)
Article
Computer Science, Artificial Intelligence
Borja Bordel, Ramon Alcarria, Joaquin Chung, Rajkumar Kettimuthu
Summary: Future Industry 4.0 scenarios require seamless integration between computational and physical processes. To achieve this, dense platforms made of small sensing nodes and resource constraint devices are widely deployed. These devices have limited computational resources and rely on powerful gateways to handle remaining operations. Security concerns, particularly in establishing secure communications among sensing nodes, have led to the promotion of a new generation of hardware devices based on Physical Unclonable Functions. These devices do not consume computational resources, but necessitate large key-value catalogues for each node, leading to scalability issues and increased processing delays. In this paper, a predictor-corrector model is proposed to address this challenge and reduce resource consumption.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2023)
Review
Chemistry, Analytical
Daniele Mazzei, Reshawn Ramjattan
Summary: This article provides a systematic review on the intersection of machine learning and industry 4.0. The study finds that security and predictive maintenance are the most common topics in the industry, and CNNs are the most widely used machine learning method. Additionally, there is a difference in focus between academia and industry, with the industry placing more emphasis on successful adoption rather than building better machine learning models.
Article
Environmental Sciences
Chor Gene Cheah, Wen Yi Chia, Shuet Fen Lai, Kit Wayne Chew, Shir Reen Chia, Pau Loke Show
Summary: The Industrial Revolution 4.0 (IR 4.0) offers opportunities to improve solid waste management through digital and machinery applications. The use of IR 4.0 technologies, such as machine learning and RFID, can automate waste segregation and enable traceability in materials, leading to more efficient waste management. However, there is a lack of coherency in applying these technologies on a larger scale, which calls for a comprehensive end-to-end integration for optimizing the entire solid waste management chain.
ENVIRONMENTAL RESEARCH
(2022)
Article
Chemistry, Analytical
Eddi Miller, Vladyslav Borysenko, Moritz Heusinger, Niklas Niedner, Bastian Engelmann, Jan Schmitt
Summary: The study utilizing machine learning methods for automatic detection of production machine changeover processes demonstrated good model performance, with the Random Forest ML model achieving the best results of 97% F1 score and 99.72% AUC score. Optimal model performance was found when considering a binary classification of changeover and production phases, and reducing the subphases of the changeover process.
Article
Business
Cumali Kilic, Gaye Atilla
Summary: In the rapidly evolving business landscape, the Industry 4.0 revolution has reshaped industries and challenges prevailing business models. This research explores the interplay between Industry 4.0 technologies and organizational sustainability, providing valuable insights through real-world explorations and interviews. The findings serve as a guiding compass for businesses and decision-makers, offering clarity on potential benefits and challenges associated with Industry 4.0 adoption.
BUSINESS STRATEGY AND THE ENVIRONMENT
(2023)
Article
Environmental Sciences
Rabia Qammar, Zain Ul Abidin, Shrafat Ali Sair, Ijaz Ahmad, Ala'a Zuhair Mansour, Hodifah Farhan Ahmad Abu Owidha
Summary: This study investigates the impact of waste management in the context of Industry 4.0 and sustainable development. The findings suggest that Industry 4.0 and waste management significantly contribute to achieving sustainable development. The integration of Industry 4.0 technologies and effective waste management practices can help organizations implement sustainable development goals.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Agricultural Engineering
Zafar Said, Prabhakar Sharma, Quach Thi Bich Nhuong, Eric Lichtfouse, Haris M. Khalid, Rafael Luque, Xuan Phuong Nguyen, Anh Tuan Hoang, Bhaskor J. Bora
Summary: Food waste is a serious environmental and social issue, and efficient management using emerging technologies like IoT, AI, and ML is necessary. By analyzing IoT sensor data using AI and ML techniques, real-time decision-making and process optimization can be achieved, leading to improved bioprocess monitoring and the generation of value-added products and chemicals, thus contributing to environmental sustainability and food security.
BIORESOURCE TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Javier Rubio-Loyola, Wolph Ronald Shwagger Paul-Fils
Summary: Industry 4.0 relies heavily on sensor data analytics, especially in predicting black carbon emission (EoBC) during the operation of Industrial Furnaces (IFs). This paper introduces a methodological approach using machine learning (ML) predictive models to forecast EoBC in IFs.
Review
Engineering, Chemical
Abdul Quadir Md, Keshav Jha, Sabireen Haneef, Arun Kumar Sivaraman, Kong Fah Tee
Summary: In manufacturing, maintaining product quality is crucial for staying ahead of the competition. Manufacturers invest resources in quality control and assurance to improve the quality of their products. Visual quality inspections can cause bottlenecks before and after assembly, prompting the use of advanced sensors and machine learning techniques to enhance quality.
Review
Chemistry, Analytical
Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
Summary: Digital twin and artificial intelligence technologies play essential roles in Industry 4.0, with a focus on the integration of infrastructure, algorithms, and applications. AI-driven digital twin technologies are widely used in smart manufacturing and advanced robotics, offering advantages for sustainable development.
Article
Computer Science, Hardware & Architecture
Jesus N. S. Rubi, Paulo H. P. de Carvalho, Paulo R. L. Gondim
Summary: This research focuses on the application of Internet of Forest Things (IoFT) in predicting wildfire behavior, and proposes a semantic platform for aggregating heterogeneous data and achieving interoperability through semantic technologies. By using machine learning techniques, the study predicted the areas affected after a fire event based on climatic-and vegetation-related data gathered by Brazilian government sensors and satellite information. The validation showed the effectiveness of the proposed platform and predictions.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Green & Sustainable Science & Technology
Tonni Agustiono Kurniawan, Christia Meidiana, Mohd Hafiz Dzarfan Othman, Hui Hwang Goh, Kit Wayne Chew
Summary: Malang, Indonesia has faced environmental problems due to excessive municipal solid waste. China's smart city, Nanning, can provide valuable experiences for Malang in improving waste management through digitalization, leading to a transition towards a circular economy.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Business
Dilupa Nakandala, Richard Yang, Henry Lau, Samanthi Weerabahu
Summary: This study investigates the effects of Industry 4.0 technologies on business operations and supply chain resilience. The findings show that Industry 4.0 technologies have a direct and positive impact on supply chain resilience, and incremental innovation acts as a mediator for this relationship. This research contributes to the literature on resilience, organizational capability, and innovation.
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL
(2023)
Article
Biotechnology & Applied Microbiology
Oliver J. Fisher, Nicholas J. Watson, Laura Porcu, Darren Bacon, Martin Rigley, Rachel L. Gomes
Summary: Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilized within data-driven models (DDM). This study proposes a framework for developing data-driven models of bioprocesses and evaluates it by modeling an industrial bioprocess that treats industrial wastewater and generates bioenergy. The best performing model, a stacked neural network model, accurately predicts the reduction in chemical oxygen demand for the wastewater in both testing and unseen data.
BIOCHEMICAL ENGINEERING JOURNAL
(2022)
Review
Engineering, Environmental
Zhongli Wang, Yanming Wang, Rachel L. Gomes, Helena Gomes
Summary: The article discusses the application and pollution issues of selenium, introduces different types of selenium remediation techniques, and evaluates their potential for selenium recovery. It is found that biological selenium oxyanions reduction method is a cost-effective selenium remediation method, simultaneously generating biosynthetic selenium nanoparticles.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Review
Environmental Sciences
Max D. Gillingham, Rachel L. Gomes, Rebecca Ferrari, Helen M. West
Summary: Recent research on magnetisation of biochar has opened new opportunities in environmental remediation by simplifying its separation process and addressing waste management and nitrogen pollution. However, further studies are needed to understand the impacts of biochar on soil chemistry and biology to protect and support soil ecosystems.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Materials Science, Multidisciplinary
Edward Acheampong, Eric Okyere, Samuel Iddi, Rachel L. Gomes, Joseph H. K. Bonney, Joshua Kiddy K. Asamoah, Jonathan A. D. Wattis
Summary: This study develops a modified compartmental model to describe the dynamics of SARS-CoV-2 transmission in Ghana and performs a detailed analysis of the model. The study shows that the disease-free equilibrium is globally asymptotically stable when the basic reproduction number (R0) is less than 1. The model is parameterized using reported data and shows good agreement. Additionally, the testing rate has a significant influence on R0.
RESULTS IN PHYSICS
(2022)
Article
Engineering, Environmental
Thomas Stanton, Guaduneth Chico, Elizabeth Carr, Sarah Cook, Rachel Louise Gomes, Elizabeth Heard, Antonia Law, Hazel L. Wilson, Matthew Johnson
Summary: This article presents a detailed citizen science survey conducted by the non-profit organization Planet Patrol in the United Kingdom, focusing on anthropogenic litter (AL) in freshwater, terrestrial, and coastal environments. Plastic was found to be the dominant type of litter, with beverage containers and non-beverage packaging being the most common uses. The branded litter was associated with companies such as The Coca-Cola Company, Anheuser-Busch InBev, and PepsiCo. The article also discusses the Environmental Social Governance (ESG) statements of these companies and upcoming UK legislation.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Environmental Sciences
Andrea-Lorena Garduno-Jimenez, Juan Carlos Duran-Alvarez, Ruth Silvana Cortes-Lagunes, David A. Barrett, Rachel L. Gomes
Summary: This study investigates the sorption characteristics of tramadol in different soils and finds that soils with higher clay content have stronger sorption capacity for tramadol. The study also reveals that tramadol from wastewater effluent is more readily adsorbed by soils, indicating that clay soils can effectively retain tramadol from irrigation water.
Article
Environmental Sciences
Michelle Baker, Alexander D. Williams, Steven P. T. Hooton, Richard Helliwell, Elizabeth King, Thomas Dodsworth, Rosa Maria Baena-Nogueras, Andrew Warry, Catherine A. Ortori, Henry Todman, Charlotte J. Gray-Hammerton, Alexander C. W. Pritchard, Ethan Iles, Ryan Cook, Richard D. Emes, Michael A. Jones, Theodore Kypraios, Helen West, David A. Barrett, Stephen J. Ramsden, Rachel L. Gomes, Chris Hudson, Andrew D. Millard, Sujatha Raman, Carol Morris, Christine E. R. Dodd, Jan-Ulrich Kreft, Jon L. Hobman, Dov J. Stekel
Summary: Waste from dairy production is a significant source of contamination from antimicrobial resistant bacteria and genes. Storing slurry waste for at least 60 days can significantly reduce the spread of ARB onto land. Furthermore, further reductions in AMR are unlikely on farms with low antibiotic use.
ENVIRONMENT INTERNATIONAL
(2022)
Article
Environmental Sciences
Andrea-Lorena Garduno-Jimenez, Juan-Carlos Duran-Alvarez, Rachel Louise Gomes
Summary: The first meta-analysis and modeling study of pharmaceuticals' soil/water partitioning based on batch-sorption literature is presented. The analysis indicates that batch-sorption studies have limitations in evaluating partitioning under environmentally-relevant conditions. Suggestions are made to utilize environmentally relevant pharmaceutical concentrations, perform batch sorption studies at different temperatures, and use specific methodology for comparison and modeling. The study identifies important variables, such as pharmaceutical solubility, soil characteristics, and experimental conditions, for predicting soil/water partitioning.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Engineering, Environmental
Okechukwu Okorie, Jennifer Russell, Ruth Cherrington, Oliver Fisher, Fiona Charnley
Summary: This study explores the potential role of digital technologies in cultivating competitive advantage for manufacturing firms in terms of achieving net-zero emissions. The integration of digital technologies can help firms achieve zero emissions and gain a competitive edge. The study also emphasizes the importance of managing and developing intangible assets, including labor and supply chain relationships.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Materials Science, Textiles
Oliver J. Fisher, Ahmed Rady, Aly A. A. El-Banna, Nicholas J. Watson, Haitham H. Emaish
Summary: Egyptian cotton, known for its quality, is a vital component of Egypt's economy and is traditionally graded through manual inspection. However, manual grading has its limitations, such as labor-intensive requirements and low efficiency. This study proposes a cost-effective alternative using classification models and image processing techniques to grade Egyptian cotton. Three supervised machine learning algorithms were evaluated, with random forest showing the highest accuracy (82.13-90.21%).
TEXTILE RESEARCH JOURNAL
(2023)
Article
Chemistry, Analytical
Oliver J. Fisher, Ahmed Rady, Aly A. A. El-Banna, Haitham H. Emaish, Nicholas J. Watson
Summary: This study examines the application of semi-supervised and active learning in the labelling of cotton lint samples to achieve accurate classification while reducing the amount of labelled data required. The results show that active learning models can achieve higher accuracy while reducing the data volume, demonstrating the potential for developing accurate and efficient machine learning models for grading food and industrial crops.
Article
Environmental Sciences
Vivek Agarwal, Amit Kumar, Zhengyuan Qin, Rachel L. Gomes, Stuart Marsh
Summary: This study analysed the land movement in Battersea, London, and found an average land subsidence rate of -6.8±1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. It underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence, and emphasizes the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability.
Article
Materials Science, Multidisciplinary
John Luke Woodliffe, Amy-Louise Johnston, Michael Fay, Rebecca Ferrari, Rachel L. Gomes, Ed Lester, Ifty Ahmed, Andrea Laybourn
Summary: Metal-organic frameworks (MOFs) have high potential for carbon dioxide capture due to their sorption capacities. However, they are thermally insulating, making thermal CO2 regeneration challenging. This study presents magnetic framework composites (MFCs) that include magnetic nanoparticles within MOF structures for efficient CO2 regeneration. The MFCs show high CO2 adsorption capacities and rapid heating capabilities, produced in a sustainable and scalable manner.
MATERIALS ADVANCES
(2023)
Proceedings Paper
Automation & Control Systems
Peter J. Craigon, Debra Fearnshaw, Oliver J. Fisher, Emma Hadfield-Hudson
Summary: This extended abstract presents the Equality, Diversity and Inclusion (EDI) cards, discussing their structure, layout, and initial testing methods. These cards are designed to assist researchers in engaging with questions around EDI in their work.
FIRST INTERNATIONAL SYMPOSIUM ON TRUSTWORTHY AUTONOMOUS SYSTEMS, TAS 2023
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Martin Trullenque Ortiz, Oriol Sallent, Daniel Camps-Mur, Josep Escrig Escrig, Carlos Herranz
Summary: This paper investigates the impact of urban traffic congestion on network overload and proposes a load balancing approach to mitigate the demand for radio resources. The results show that this approach significantly alleviates the overload situation.
2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING)
(2022)
Article
Computer Science, Interdisciplinary Applications
Nohan Joemon, Melpakkam Pradeep, Lokesh K. Rajulapati, Raghunathan Rengaswamy
Summary: This paper introduces a smoothing-based approach for discovering partial differential equations from noisy measurements. The method is data-driven and improves performance by incorporating first principles knowledge. The effectiveness of the algorithm is demonstrated in a real system using a new benchmark metric.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhibin Lu, Yimeng Li, Chang He, Jingzheng Ren, Haoshui Yu, Bingjian Zhang, Qinglin Chen
Summary: This study proposes a new inverse design method using a physics-informed neural network to identify optimal heat sink designs. A hybrid PINN accurately approximates the governing equations of heat transfer processes, and a surrogate model is constructed for integration with optimization algorithms. The proposed method accelerates the search for Pareto-optimal designs and reduces search time. Comparing different scenarios facilitates real-time observation of multiphysics field changes, improving understanding of optimal designs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Luca Gasparini, Antonio Benedetti, Giulia Marchese, Connor Gallagher, Pierantonio Facco, Massimiliano Barolo
Summary: In this paper, a method for batch process monitoring with limited historical data is investigated. The methodology utilizes machine learning algorithms to generate virtual data and combines it with real data to build a process monitoring model. Automatic procedures are developed to optimize parameters, and indicators and metrics are proposed to assist virtual data generation activities.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Julia Jimenez-Romero, Adisa Azapagic, Robin Smith
Summary: Energy transition is a significant and complex challenge for the industry, and developing cost-effective solutions for synthesizing utility systems is crucial. The research combines mathematical formulation with realistic configurations and conditions to represent utility systems and provides a basis for synthesizing energy-efficient utility systems for the future.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Samuel Adeyemo, Debangsu Bhattacharyya
Summary: This work develops algorithms for estimating sparse interpretable data-driven models. The algorithms select the optimal basis functions and estimate the model parameters using Bayesian inferencing. The algorithms estimate the noise characteristics and model parameters simultaneously. The algorithms also exploit prior analysis and special properties for efficient pruning, and use a modified Akaike information criterion for model selection.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Abbasali Jafari-Nodoushan, Mohammad Hossein Dehghani Sadrabadi, Maryam Nili, Ahmad Makui, Rouzbeh Ghousi
Summary: This study presents a three-objective model to design a forward supply chain network considering interrelated operational and disruptive risks. Several strategies are implemented to cope with these risks, and a joint pricing strategy is used to enhance the profitability of the supply chain. The results show that managing risks and uncertainties simultaneously can improve sustainability goals and reduce associated costs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
T. A. Espaas, V. S. Vassiliadis
Summary: This paper extends the concept of higher-order search directions in interior point methods to convex nonlinear programming. It provides the mathematical framework for computing higher-order derivatives and highlights simplified computation for special cases. The paper also introduces a dimensional lifting procedure for transforming general nonlinear problems into more efficient forms and describes the algorithmic development required to employ these higher-order search directions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
David A. Linan, Gabriel Contreras-Zarazua, Eduardo Sanhez-Ramirez, Juan Gabriel Segovia-Hernandez, Luis A. Ricardez-Sandoval
Summary: This study proposes a parallel hybrid algorithm for optimal design of process flowsheets, which combines stochastic method with deterministic algorithm to achieve faster and improved convergence.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaoyong Lin, Zihui Li, Yongming Han, Zhiwei Chen, Zhiqiang Geng
Summary: A novel GAT-LSTM model is proposed for the production prediction and energy structure optimization of propylene production processes. It outperforms other models and can provide the optimal raw material scheme for actual production processes.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangoz, Ali Mesbah, Fani Boukouvala, Fernando Lima, Antonio del Rio Chanona, Christos Georgakis
Summary: This paper provides a concise perspective on the potential of machine learning in the PSE domain, based on discussions and talks during the FIPSE 5 conference. It highlights the need for domain-specific techniques in molecular/material design, data analytics, optimization, and control.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hesam Hassanpour, Prashant Mhaskar, Brandon Corbett
Summary: This work addresses the problem of designing an offset-free implementable reinforcement learning (RL) controller for nonlinear processes. A pre-training strategy is proposed to provide a secure platform for online implementations of the RL controller. The efficacy of the proposed approach is demonstrated through simulations on a chemical reactor example.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hunggi Lee, Donghyeon Lee, Jaewook Lee, Dongil Shin
Summary: This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, mitigating the risk of major industrial disasters, and providing faster and higher-resolution results.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Sibel Uygun Batgi, Ibrahim Dincer
Summary: This study examines the environmental impacts of three alternative hydrogen-generating processes and determines the best environmentally friendly option for hydrogen production by comparing different impact categories. The results show that the solar-based HyS cycle options perform the best in terms of global warming potential, abiotic depletion, acidification potential, ozone layer depletion, and human toxicity potential.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
LaGrande Gunnell, Bethany Nicholson, John D. Hedengren
Summary: A review of current trends in scientific computing shows a shift towards open-source and higher-level programming languages like Python, with increasing career opportunities in the next decade. Open-source modeling tools contribute to innovation in equation-based and data-driven applications, and the integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to significantly accelerate progress, but long-term support mechanisms are still necessary.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniel Cristiu, Federico d'Amore, Fabrizio Bezzo
Summary: This study presents a multi-objective mixed integer linear programming framework to optimize the supply chain for mixed plastic waste in Northern Italy. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximizing gross profit and minimizing greenhouse gas emissions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
