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Introduction—Electricity’s Attributes
Published in Clark W. Gellings, 2 Emissions with Electricity, 2020
Three sectors of the U.S. economy are particularly sensitive to power disturbances: The digital economy (DE). This sector includes firms that rely heavily on data storage and retrieval, data processing, or research and development operations. Specific industries include telecommunications, data storage and retrieval services (including collocation facilities or Internet hotels), biotechnology, electronics manufacturing, and the financial industry.Continuous process manufacturing (CPM). This sector includes manufacturing facilities that continuously feed raw materials, often at high temperatures, through an industrial process. Specific industries include paper; chemicals; petroleum; rubber and plastic; stone, clay, and glass; and primary metals.Fabrication and essential services (F&ES). This sector includes all other manufacturing industries, plus utilities and transportation facilities such as railroads and mass transit, water and wastewater treatment, and gas utilities and pipelines.
The Internet of Things Applications
Published in Ravi Ramakrishnan, Loveleen Gaur, Internet of Things, 2019
Ravi Ramakrishnan, Loveleen Gaur
Process manufacturing refers to using a process plant to take in inputs and using defined formulas and recipes to generate outputs in the form of main products or by-products, generating effluents and wastages in the process where the net sum of inputs should be ideally equal to the net sum of outputs. It is typically used in petrochemical refineries, chemical industries, food and beverages, and the consumer packaging industry. The ideal formulas and recipes for bulk manufacturing stays the same and, generally, the output of the previous stage forms the input for the subsequent stage.
Platform-based product development in the process industry: a systematic literature review
Published in International Journal of Production Research, 2023
Rasmus Andersen, Thomas Ditlev Brunoe, Kjeld Nielsen
Based on the introduction, it is apparent that traditional product development and manufacturing approaches are unfit for today's ever-increasing market needs. This was shown to be the case regardless of whether the manufacturer is in the discrete manufacturing or process manufacturing industry. Examples from literature and industry alike showed that platform-based product development has proven a successful approach for accommodating the needs and challenges of today's markets. Even so, documented examples of platform-based product development approaches from the process industry are very sparse. This is despite evidence suggesting that this industry may also derive benefits from adopting platform-based approaches to product development. Based on these preliminary findings, the research question for this study is defined as:
An energy-consumption model for establishing an integrated energy-consumption process in a machining system
Published in Mathematical and Computer Modelling of Dynamical Systems, 2020
Wenbin Gu, Zhuo Li, Zeyu Chen, Yuxin Li
In recent years, with the increasingly severe energy consumption and environmental problems in the manufacturing industry, the energy consumption of machining processes has become a hot issue in the field of green manufacturing [1]. The machining process refers to the process of changing the blank size, shape, mutual position and surface quality by machining, so as to make it a qualified part [2–4]. However, in the past, it was always considered that the energy consumption of machining equipment in discrete manufacturing was relatively small compared to the process manufacturing industries such as the steel industry. Therefore, the industries and the academics pay insufficient attention to the energy consumption and efficiency of the machining processes, resulting in the lack of relevant research [5–7]. Gutowski et al. (2013) [8] pointed out that the CO2 emissions of a CNC machine tool with main shaft power in 22 kW operating 1 year was equivalent to the emissions of 61 SUV cars. China has the largest number of machine tools in the world (more than 7 million units), and the total energy consumption are huge. If the average-rated power of each machine is calculated at 10 kW, the total power is about 70TW, which is about 3000 times of the total installed capacity of the Three Gorges Power Station [9].
The influence of intelligent manufacturing on financial performance and innovation performance: the case of China
Published in Enterprise Information Systems, 2020
Jie Yang, Limeng Ying, Manru Gao
Intelligent manufacturing is an evolving concept. As described by Intelligent Manufacturing Development Plan (2016–2020) of China, intelligent manufacturing refers to a new production mode based on the deep integration of new-generation information and communication technology and advanced manufacturing technology, which runs through design, manufacturing, management, service and other process of manufacturing activities, featuring self-sensing, self-learning, self-decision, self-executing and self-adaption (Zhong et al. 2017; Li 2018). The vigorous development of intelligent manufacturing has incredibly promoted a large number of advanced business models and new industries, which in turn continuously injected fresh vitality into manufacturing industry (Müller, Buliga, and Voigt 2018a). It is undoubtedly a driving force for change and provides giant space for development. Intelligent manufacturing has been directed as a national strategy for upgrading industries and technological innovation in various developed countries, such as Germany, USA, UK, Japan, France, Italy, Belgium and Spain (Zhong et al. 2017; Grant and Yeo 2018). Through the introduction of cyber-physical systems, the cyber space and physic world are highly integrated (Tao et al. 2018), and new manufacturing modes such as process manufacturing, discrete manufacturing, networked collaborative manufacturing, mass customisation, and remote operation service are explored (Zhou et al. 2018).