Ambient intelligence for optimal manufacturing and energy efficiency

Abstract

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 (AmI) 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 AmI 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.

Making Decisions About Saving Energy in Compressed Air Systems Using Ambient Intelligence and Artificial Intelligence

“…Increasing complexity and demand for high productivity have led to growing applications of sensor networks to enable more reliable, timely, and comprehensive information from machines [4], [5]. Both classical measurement of energy consumption and modern sensory solutions with remote observation will be used.…”

“…Ambient information and knowledge gathered within a manufacturing environment are untapped resources for optimising energy use in real time and providing energy efficient manufacturing. The proposed approach described in this short paper is to investigate ambient sensing and artificial intelligence (AI) for manufacturing units [5] that interact with people and sensors to produce a detailed awareness of the process and environment, and to complement this with Knowledge Management (KM) systems to solve problems and provide suggestions; a holistic approach that takes advantage of all available relevant data / information [6].…”

Introducing dead bands within two-dimensional clusters of user data to improve data classification

“…The work used a keyboard and mouse but on-going research is experimenting with different sensors [14,15] and user interfaces [ 16,17] such as joysticks [18][19][20][21][22][23] and the use of Al is being trialed [24][25][26][27][28][29][30][31][32], using Blackboard systems [33,34], ANNs [35][36][37] and fuzzy systems [38][39][40][41] with a view to improving the gathering of user data and identification of correlations between relevance ratings for WWW pages and their actual perceived usefulness. The methods are also being introduced to a simulation based robot command library [42].…”

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

“…e path planning problem aims to find the safest and shortest path autonomously without collisions from the start point to the target point under a given environment with barriers [2,3]. Path planning has been widely used in fields such as logistics distribution, intelligent transportation, and weapons navigation [4][5][6].…”