Clustering and optimising regional segregated resource allocation networks.

Policymakers and officials worldwide are making more stringent environmental norms and waste disposal policies to encourage industries to move towards cleaner production. One of the main challenges that industries face moving towards cleaner production is the adoption of different strategies for optimising their resource utilisation and waste reduction economically. This is particularly challenging for large-scale industries or a group of industrial plants located in an industrial region. This paper presents a novel approach to economic resource optimisation focussed mainly on large-scale industries, different industrial plants located in the vicinity of each other, or an industrial symbiosis network. In this work, a clustering algorithm is developed to segregate the given plants into different clusters based on the concept of load deficits and surpluses of each plant. The concept ideally allows only the plants with surpluses to send out their unused sources and plants with deficits to only receive external sources/resources. The clusters are formed based on the distances between plants, which in turn helps in saving transportation and communication costs. The clustered plants are then easy to optimise and manage for resource and cost optimality. The applicability of the proposed clustering algorithm is demonstrated using two case studies from the domain of water recycling networks containing multiple contaminants with detailed network design, highlighting the importance of clustering in an industrial symbiosis network. It is observed that directing the excess flows from one plant to other plants in the same cluster can save a considerable amount of fresh resources. It implies that in the broader aspect, the developed methodology can address the optimisation of economic resources and can aid in the better management of overall resources for a large-scale industrial symbiosis network.

Industrial site water exchange network synthesis considering multiple quality constraints and water headers.

This work aims to extend the previous Pinch Analysis framework to the industrial site material recycling network with site headers synthesis from single quality to multiple qualities. The analysis provides guided resources management strategy in any eco-industrial park to reduce the reliance on raw resources that are extracted from the environment. The Pinch Point(s) are first identified for the overall network using the Material Recovery Pinch Diagram for all the qualities. The guideline for the cross-plant material sources transfer is then built upon the concept of the Pinch Point(s) for all the qualities to minimise the cross-plant source transfer or a number of connections. An iterative header targeting framework is then proposed to determine the flowrates and the qualities of the headers. Two case studies, which have single and multiple qualities Total Site water recycling network, are used to demonstrate the proposed framework, comparing results obtained using direct integration and centralised headers. The single quality case results in 4.1% lower fresh resource intake compared to without cross-transfer, while the multiple qualities case could have 5.3% lower fresh resources for two and three plants scenarios. This framework provides a proper analysis of the problem, which allows users to gain insights on the effective cross-plant source transfer schemes with headers constraint by resource qualities.

Asset maintenance optimisation approaches in the chemical and process industries - A review.

The operational performance of a chemical process plant highly depends on the assets' condition and maintenance practices. As chemical processes are highly complex systems, increasing the risk frequencies and their interactions, the maintenance planning becomes crucial for stable operation. This paper provides a critical analysis of the recently developed approaches for asset maintenance approaches in the chemical industry. The strategies include corrective maintenance, time-based, risk-based, condition-based and opportunistic maintenance. Various methods on selecting the optimal maintenance strategy are discussed as well. This paper also evaluates reliability issues in chemical plants and integrated sites encompassing the maintenance optimisation. Several directions for potential future improvements are proposed based on this analysis, as follows: (i) potential study of exploiting production or other opportunities to postpone or conduct earlier maintenance; (ii) joint optimisation of spare part ordering strategy and data-driven maintenance planning study is needed; (iii) fault propagation modelling of structural dependent units to facilitate proper maintenance planning; (iv) a framework or tool that consider quantitative and qualitative time-variant data inputs is lacking for business-informed asset maintenance.