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A Framework for Quantifying Energy and Productivity Benefits of Smart Manufacturing Technologies

机译:量化智能制造技术的能源和生产率收益的框架

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The transformative possibilities of smart manufacturing, which is defined by digitization, enhanced connectivity, advanced analytics, and integrated cyber-physical systems in manufacturing processes and systems, have been extensively discussed in the literature. Potential benefits underscored include cost reduction, production flexibility, shorter product times-to-market, energy efficiency, environmental impact reduction, and increased productivity. Scant attention has been given to formal methods for quantifying and analyzing energy productivity to aid a broad range of manufacturing industries in evaluating the merits of adoption. Here we present a strategic analysis framework to estimate cost-effective improvements in energy efficiency and productivity that may be realized through smart manufacturing. The framework uses the cost of conserving energy (CCE) as a complementary measure to determine the feasibility of a set of smart manufacturing interventions in the context of a specific factory, firm, or entire industry. Guided by a function-driven approach based on key performance indicators, the CCE measure accounts for system-wide changes in direct and indirect costs and total energy use relative to the same manufacturing system without smart manufacturing interventions. These changes include cost of cyber-physical systems, which include components such as sensors, controllers, smart equipment, and information and communication technology (ICT) equipment. Additionally, cost and savings are accounted from changes in primary and intermediate inputs, production time, off-spec production, and waste. Energy calculations include upstream energy use of the cyber-physical systems. For complex processes with significant circularity and/or feedbacks, we propose the use of input-output models to characterize elements of the CCE. The framework is discussed in the context of recent efforts in craft breweries to use smart manufacturing for reducing energy intensity and increasing product yield.
机译:通过数字化,增强的连接性,先进的分析以及制造过程和系统中集成的网络物理系统定义的智能制造的变革可能性已在文献中进行了广泛讨论。强调的潜在好处包括降低成本,生产灵活性,缩短产品上市时间,能源效率,减少环境影响以及提高生产率。人们很少关注量化和分析能源生产率的正式方法,以帮助广泛的制造业评估采用的优劣。在这里,我们提出了一个战略分析框架,以估算可以通过智能制造实现的能效和生产率方面的成本有效改进。该框架使用节约能源成本(CCE)作为补充措施,以确定在特定工厂,公司或整个行业的背景下进行一系列智能制造干预的可行性。在基于关键绩效指标的功能驱动方法的指导下,CCE指标可说明系统范围内相对于同一制造系统的直接和间接成本以及总能耗的变化,而无需进行智能制造干预。这些变化包括网络物理系统的成本,其中包括传感器,控制器,智能设备以及信息和通信技术(ICT)设备等组件。此外,成本和节省是通过更改主要和中间投入,生产时间,不合规格的生产以及浪费来实现的。能源计算包括网络物理系统的上游能源使用。对于具有明显循环性和/或反馈的复杂过程,我们建议使用输入输出模型来表征CCE的元素。该框架是在精酿啤酒厂最近的努力中使用智能制造来降低能源强度并提高产品产量的背景下进行讨论的。

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