New efficiency opportunities arising from intelligent real time control tools applications: the case of Compressed Air Systems’ energy efficiency in production and use

Bonfá, Francesca; Benedetti, Miriam; Ubertini, Stefano; Introna, Vito; Santolamazza, Annalisa

doi:10.1016/j.egypro.2019.01.809

Cited by 17 publications

(25 citation statements)

References 1 publication

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“…Finally, the interconnection within the TBL identified from the studied literature could provide practitioners with more confidence on the positive relationship between the three pillars, especially from the green-lean integration [55,80,130], highlighting that introducing digital technologies to improve efficiency and productivity towards economic sustainability does not necessarily conflict with environmental sustainability. Understanding the interconnection within the TBL can support practitioners to expand the use of digitalization as means to increase the environmental sustainability of the manufacturing industry, thereby achieving more sustainable manufacturing.…”

Section: Discussionmentioning

confidence: 99%

“…Santos et al (2019) show a dramatic reduction in the energy bill of €500 per year reached by the "plug&glean" implementation to track energy consumption [53]. Similarly, Bonfá et al achieve a 10% energy saving by monitoring and adjusting the supply of compressed air, based on air pressure and variation of temperature demand [80]. Furthermore, the case study in [13] demonstrates a 20% reduction by tracking and identifying improvement potential [13].…”

Section: Cps and Connection Level (Iiot)mentioning

confidence: 98%

“…[58] CPS P, T +++ [68] Big Data P ++ + [69] Industry 4.0 P +++ [70] Cloud P ++ ++ [71] Big Data P ++ [72] CPS P +++ +++ [73] CPS P ++ ++ [74] Big Data T ++ ++ [75] Cloud, CPS, IoT P +++ ++ [76] Big Data P ++ [77] IAI, Big Data P ++ + [78] CPS P +++ ++ [79] CPS, VR, AR, IoT P ++ ++ [80] CPS P ++ [81] CPS P ++ [82] AM T [83] Industry 4.0 P ++ + [17] Big Data T + [84] Industry Big Data P ++ + [69] Industry 4.0 P +++ [70] Cloud P ++ ++ [71] Big Data P ++ [72] CPS P +++ +++ [73] CPS P ++ ++ [74] Big Data T ++ ++ [75] Cloud, CPS, IoT P +++ ++ [76] Big Data P ++ [77] IAI, Big Data P ++ + [78] CPS P +++ ++ [79] CPS, VR, AR, IoT P ++ ++ [80] CPS P ++ [ [68] Big Data P ++ + [69] Industry 4.0 P +++ [70] Cloud P ++ ++ [71] Big Data P ++ [72] CPS P +++ +++ [73] CPS P ++ ++ [74] Big Data T ++ ++ [75] Cloud, CPS, IoT P +++ ++ [76] Big Data P ++ [77] IAI, Big Data P ++ + [78] CPS P +++ ++ [79] CPS, VR, AR, IoT P ++ ++…”

Section: Mapping Of Literature Analysismentioning

confidence: 99%

“…Overall, it could be argued that environmental benefits can be aligned to economic sustainability benefits easier than to social sustainability, as more papers are showing correlations between the first two mentioned pillars. This phenomenon can be explained from the lean-green [53,78] and resource and energy efficiency perspectives [9,77,80]. Lean-green management has been introduced as an effective way to improve environmental and economic efficiency in manufacturing [130], because the principle of eliminating waste from lean has mainly positive impacts on improving environmental performance [53].…”

Section: Implications From the Interconnection Within The Tblmentioning

confidence: 99%

See 3 more Smart Citations

Environmental Sustainability of Digitalization in Manufacturing: A Review

2020

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The rapid development and implementation of digitalization in manufacturing has enormous impact on the environment. It is still unclear whether digitalization has positive or negative environmental impact from applications in manufacturing. Therefore, this study aims to discuss the overall implications of digitalization on environmental sustainability through a literature study, within the scope of manufacturing (product design, production, transportation, and customer service). The analysis and categorization of selected articles resulted in two main findings: (1) Digitalization in manufacturing contributes positively to environmental sustainability by increasing resource and information efficiency as a result of applying Industry 4.0 technologies throughout the product lifecycle; (2) the negative environmental burden of digitalization is primarily due to increased resource and energy use, as well as waste and emissions from manufacturing, use, and disposal of the hardware (the technology lifecycle). Based on these findings, a lifecycle perspective is proposed, considering the environmental impacts from both the product and technology lifecycles. This study identified key implications of digitalization on environmental sustainability in manufacturing to increase awareness of both the positive and negative impacts of digitalization and thereby support decision making to invest in new digital technologies.

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Section: Discussionmentioning

confidence: 99%

Section: Cps and Connection Level (Iiot)mentioning

confidence: 98%

Section: Mapping Of Literature Analysismentioning

confidence: 99%

Section: Implications From the Interconnection Within The Tblmentioning

confidence: 99%

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Environmental Sustainability of Digitalization in Manufacturing: A Review

2020

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“…Energy and performance optimization of CAS with machine learning was investigated by Santolamazza [25][26][27]. In [25], a methodological approach that could be applied to monitor and control energy performance in industrial plants was presented.…”

Section: Innovations In Energy Managementmentioning

confidence: 99%

Intelligent Energy Management of Compressed Air Systems

Thabet

Sanders

Becerra

et al. 2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

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This work considers the use of real time sensing and AI (machine learning) to increase Compressed Air Systems (CAS) efficiency. Algorithms that automate the detection of energy inefficiencies and make decisions regarding suitable troubleshooting procedure will be created. Systems using compressed air are often inefficient and expensive to operate. Less than one unit of energy is turned into useful compressed air for every ten provided. Intelligent systems will be used to reduce energy consumption in compressors by considering real-time circumstances and the predicted needs. Sensor data will deliver information about the real time performance. AI will interpret the data and then act automatically. New intelligent techniques will be applied to save energy. This paper presents a review of the recent literature covering the topic of CAS energy efficiency. Some gaps in research were identified in the area of developing technologies and methods to detect and treat energy inefficiencies in CAS.

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Management of Compressed Air to Reduce Energy Consumption Using Intelligent Systems

Thabet

Sanders

Haddad

et al. 2020

Advances in Intelligent Systems and Computing

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This research investigated the use of intelligent systems for reducing energy consumption in compressed air systems. An initial literature review has been completed and mathematical models that describe typical compressed air components (compressor, tank, piping network, etc.) were created. The investigations suggested that energy used or wasted in connection with compressed air was a valuable research area to attempt to save energy. The research progressed to investigating ways of minimising energy use for air compressors based on real-time conditions (including anticipated future requirements), using intelligent systems to monitor and make decisions.

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New efficiency opportunities arising from intelligent real time control tools applications: the case of Compressed Air Systems’ energy efficiency in production and use

Cited by 17 publications

References 1 publication

Environmental Sustainability of Digitalization in Manufacturing: A Review

Environmental Sustainability of Digitalization in Manufacturing: A Review

Intelligent Energy Management of Compressed Air Systems

Management of Compressed Air to Reduce Energy Consumption Using Intelligent Systems

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