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Ambient intelligence for optimal manufacturing and energy efficiency

机译：环境智能可实现最佳制造和能源效率

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Purpose - This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (Aml) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach - Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using Aml and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings - The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications - Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications - Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications - This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value - This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined Ami and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.
机译：目的-本文旨在描述创新和智能系统的创建，以优化制造中的能源效率。该系统使用环境情报（Aml）和知识管理（KM）技术监控能耗。他们共同创建了一个决策支持系统，作为当前使用的能源管理系统的创新附件。设计/方法/方法-能耗数据（ECD）在基于面向服务的体系结构的平台中处理。该平台提供基于状态的能耗预警，与能源有关的问题的在线诊断，对生产线安装和启动阶段的支持以及对能源效率的持续改进/优化。该系统使用Aml和KM技术监控能耗。他们共同创建了一个决策支持系统，作为当前使用的能源管理系统的创新附件。调查结果-该系统提高了制造中小企业的能效。该系统提供有关能源使用的更全面信息以及一些基于知识的支持。研究限制/意义-原型系统已在一家制造公司中试用，该公司为海上石油和天然气行业（一种能源密集型制造业务）生产系泊链。本文介绍了一个案例研究，涉及能源密集型过程，涉及不同的制造概念，并涉及用于海上平台的系泊链的制造。开发该系统以支持在线检测能效问题。实际意义-可以在组装和制造过程中优化能源效率。该系统提高了制造业中小企业的能源效率。该系统提供有关能源使用的更全面信息以及一些基于知识的支持。社会影响-这项研究解决了工业生产技术中能源管理中两个最关键的问题：如何有效，及时地获取和提供在线信息以优化能耗，以及如何有效地利用这些知识来支持决策。原创性/价值-这项研究的灵感来自于工业界需要有效的能源效率工具，而工业界几乎没有采取能源效率措施的机会。这项研究结合了Ami和KM技术，并涉及传感器的新用途，包括无线智能传感器网络，以测量环境参数和条件以及处理性能和行为方面，例如在高度灵活的制造或温度分布中使用智能标签的物料流。在机器上。所获得的信息可以与标准ECD相关联，以监控能源效率并发现问题。新方法可以提供有效的方式来收集更多信息，从而使您对制造系统中的能耗有新的认识。

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