Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems

Bermeo-Ayerbe, Miguel Angel; Ocampo‐Martínez, Carlos; Díaz-Rozo, Javier

doi:10.1016/j.jclepro.2020.125556

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…The experimental results of the proposed methodology on ball milling processes showed a 3%-4% reduction in total energy consumption. Kellens et al [45] demonstrated a power measurement for a metal-cutting process to reduce standby energy and optimize the process parameters. Bermeo-Ayerbe et al [46] presented adaptive predictive control for equipment management to enhance energy efficiency.…”

Section: Smart Manufacturingmentioning

confidence: 99%

Perspectives on future research directions in green manufacturing for discrete products

Triebe¹,

Deng²,

Pérez-Cardona³

et al. 2023

Green Manuf Open

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With the increasing concern due to climate change caused by a higher atmospheric concentration of CO2 and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant amount of energy/material consumption and environmental burden and, therefore, has a great opportunity to reduce its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase the efficient usage of energy and material resources. These opportunities include designing products to consume fewer materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective from the authors on the opportunities that exist within green manufacturing for discrete products through a review of pertinent topics and future directions. The paper will focus on processes, manufacturing equipment, manufacturing systems, recovering value at a product’s end-of-life, and additional thoughts that include metrics and indicators, techno-economic assessment, and a discussion of efficiency and effectiveness. Key findings from this review include a need for social indicators and renewable energy considerations in scheduling and process planning, integrating Industry 4.0 into circular economy along with social and institutional dimensions, consistency in the ability to measure and conceptualize metrics and indicators, a detailed evaluation of the life cycle impacts and cost of Addit Manuf, and more human and environment-oriented considerations for smart manufacturing.

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Section: Smart Manufacturingmentioning

confidence: 99%

Perspectives on future research directions in green manufacturing for discrete products

Triebe¹,

Deng²,

Pérez-Cardona³

et al. 2023

Green Manuf Open

View full text Add to dashboard Cite

show abstract

“…Bermeo-Ayerbe et al. [ 74 ] proposed an adaptive predictive control mechanism that manages the devices' energy consumption profile in the industrial environment. The method reduced energy consumption by 2% and sudden power peaks of more than 11%.…”

Section: Advancement In Energy Efficiency Modellingmentioning

confidence: 99%

Post COVID-19 ENERGY sustainability and carbon emissions neutrality

Chong

Fan

Lee

et al. 2022

Energy

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“…In the case of a production enterprise, a methodology for developing it into a smart and sustainable production enterprise with sensing is developed by Chavarría-Barrientos et al [7]. Furthermore, an increasement in energy efficiency in a smart production system by controlling peripheral equipment is done by Bermeo-Ayerbe et al [4]. Alavian et al [2] developed a programmable production advisor that is able to find the current status of the system and improvements of a smart production system.…”

Section: Smart Production Systemmentioning

confidence: 99%

“…Production system Reliability Carbon Geometric Setup cost emission programming Leung [21] EPQ Production process NA Degree = 1 Variable Liu [22] NA NA NA Degree = 0 NA Liu [23] NA NA NA Signomial NA Sadjadi et al [25] NA Production process NA Considered Variable Ahmed and Sarkar [1] Sustainable NA Considered NA NA Chavarrían-Barrientos [7] Smart and sustainable NA NA NA NA Jafarian et al [17] NA NA NA Considered NA Kusiak [20] Smart NA NA NA NA Tiwari et al [31] Sustainable NA Considered NA NA Wang et al [32] Constant NA Considered NA NA Asim et al [3] Hybrid Cost NA NA Reduced Cao and Wang [5] NA NA NA Fuzzy NA Chassein and Goerigk [6] NA NA NA Robust NA Dressler et al [11] NA NA NA Considered NA El-Wakeel et al [12] NA NA NA Considered NA Ghavami et al [13] NA NA NA Fuzzy NA Guchhait et al [16] Constant Unreliable SCM NA NA NA Sarkar [26] Multi-stage, multi-cycle NA NA NA Reduced Alavian et al [2] Smart NA NA NA NA Ghobakhloo [14] Smart NA NA NA NA Nahas [24] Production line Unreliable NA NA NA Sarkar et al [27] Single-stage, clean NA NA NA Constant Sarkar and Sarkar [30] Sustainable, smart, NA Considered NA Reduced multi-stage Bermeo-Ayerbe et al [4] Smart NA NA NA NA Chen et al [8] Multi…”

Section: Table 1 Authors Contribution Tablementioning

confidence: 99%

Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

2022

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Smart manufacturing systems should always aim to be fully sustainable while simultaneously being as reliable as possible which is difficult to reach. Furthermore, climate change especially by carbon emission ejection in the industry is a significant topic and carbon emission ejection should be controlled and reduced to save the environment. Contributing towards a greener environment in a positive manner is done by reducing the amount of insufficient items that are produced in a smart production system which also can be reached with a higher reliability in the system. Therefore, this study models a smart reliable production system with controlled carbon ejection. To solve the proposed smart production system in this study, a geometric programming approach with a degree of difficulty level two is used which results into optimum results that are quasi-closed. Furthermore, numerical experiments are conducted in order to validate the proposed model and prove that by using a higher degree geometric programming approach, an optimal solution is found. The numerical results do not only show optimal solutions but also that the smart production system with controlled carbon ejection is reliable.

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Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems

Cited by 8 publications

References 24 publications

Perspectives on future research directions in green manufacturing for discrete products

Perspectives on future research directions in green manufacturing for discrete products

Post COVID-19 ENERGY sustainability and carbon emissions neutrality

Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

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