Industry 4.0 based process data analytics platform: A waste-to-energy plant case study

Kabugo, James Clovis; Jämsä‐Jounela, Sirkka‐Liisa; Schiemann, Robert; Binder, Christian

doi:10.1016/j.ijepes.2019.105508

Cited by 131 publications

(44 citation statements)

References 52 publications

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“…Before the test and results, it appeared that the impacts were negative, due to the extensive use of materials, energy, and information, and a poor discharge of waste, but the situation has been transformed into one with more positive impacts. This can be facilitated by the use of big data analytics in Industry 4.0 to produce only what is needed and factoring how the waste can be turned to energy use that does not cause pollution to the environment [112].…”

Section: Deploymentmentioning

confidence: 99%

Impact of Industry 4.0 on Environmental Sustainability

et al. 2020

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Industry 4.0 is a concept that originated from the German industry, and whose essence is the use of technology for efficient production. In business today, the emergence of Industry 4.0 for production, and its related technologies, such as the Internet of Things (IoT) and cyber-physical systems, amongst others, have, however, a negative impact on environmental sustainability as a result of air pollution, the poor discharge of waste, and the intensive use of raw materials, information, and energy. The method used in this study is an analysis of a literature review of manuscripts discussing topics related to Industry 4.0 and environmental sustainability published between 2000 and 2020. There is currently a gap existing between the actual and the desired situation, in that production occurs in a weak sustainability model, and, therefore, this research debates the effects on environmental sustainability and the challenges facing Industry 4.0. Four scenarios are discussed: a deployment scenario, an operation scenario, integration and compliance with sustainable development goals, and a long-run scenario. The results indicate that there is a negative relationship related to the flow of the production process from the inputs to the final product, including raw materials, energy requirements, information, and waste disposal, and their impacts on the environment. However, the integration of Industry 4.0 and the sustainable development goals enhance environmental sustainability to create ecological support that guarantees high environmental performance with a more positive impact than before. This paper will help stakeholders and companies to provide solutions to the existing environmental challenges that can be mediated through adopting new technologies. The novelty of this study is its depiction of Industry 4.0 and its technologies integrated with sustainable development goals to create a sustainable Industry 4.0 combining environmental protection and sustainability.

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Section: Deploymentmentioning

confidence: 99%

Impact of Industry 4.0 on Environmental Sustainability

et al. 2020

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“…While the technology evolves rather fast and it requires organizations to be adapted in a fast pace. There are a very large set of publications digging in different types of applications [45][46][47][48].…”

Section: Manufacturing Casementioning

confidence: 99%

Digitalization: An Opportunity for Contributing to Sustainability From Knowledge Creation

2020

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This paper aims at exploring the perspective of sustainability when digital transformation is adopted by one organization, although it was not the first goal targeted. Two different cases are analyzed, covering manufacturing and service industries. In those cases different factors will be analyzed, mainly focused on the positive effects of knowledge creation facilitated by direct or indirect application of digitalization. Specific analysis of different cases were carried out to identify different initiatives and the impact on environmental performance. The positive effects of the institutional dimension were also assessed.

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“…The process data analytics platform was built around the concept of industry 4.0 for studying the syngas heating values and flue gas temperatures in waste to energy plants. A neural network-NARX model developed to evaluate the performance of waste to the energy system well described the dynamic behavior of the system compared with conventional statistical techniques [1].…”

Section: Of 33mentioning

confidence: 99%

“…The key challenges industries are facing today are data collection, storage, integration, processing, and analysis. The scientific literature addressing these problems is scarce [1,2]. The analysis of such raw industrial data using advanced data analytics and AI algorithms can identify significant operational savings and suggest areas of useful technological improvements, i.e., the optimum industrial outputs, better product quality, and sustainable growth of the industries.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

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Industry 4.0 based process data analytics platform: A waste-to-energy plant case study

Cited by 131 publications

References 52 publications

Impact of Industry 4.0 on Environmental Sustainability

Impact of Industry 4.0 on Environmental Sustainability

Digitalization: An Opportunity for Contributing to Sustainability From Knowledge Creation

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

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