How digitalisation can enable industrial symbiosis practices : a case study

Industrial Symbiosis (IS) encourages a collaborative approach aiming at recovering, reprocessing and reusing non-labour resources and it is a promising solution for mitigating the rising cost of non-labour resource. Introducing IS is a knowledge intensive process and researchers have developed various information and communication (ICT) tools to support the process. However, the use of these tools in the actual industrial practice has not been adequately investigated yet. This study investigates the role that ICT tools play in facilitating the process of creating IS through a case study of International Synergies – the company which facilitated the world’s first national-level IS programme (i.e. NISP UK). Results suggest that the role of digitalisation can increase practitioners’ productivity mainly through data analytics

A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics

Industrial Symbiosis (IS) adopts a collaborative approach, which aims to re-channel resources – traditionally considered spent and non-productive – towards alternative value-adding pathways. Empirically, the concept of IS has been rapidly implemented in practice through a facilitated approach, whereby businesses are engaged and “match-made” via a facilitating body. While recommending alternative pathways for companies to establish IS-based transactions is a long-standing practice, recent technological advancement has shifted the nature of this task from one that is based purely on human intellect and reasoning, towards one which leverages intelligent recommendation algorithms to provide relevant suggestions. Traditionally, these recommendation engines rely on manually populated knowledge bases that are not only labor-intensive to build but also costly to maintain. This work presents the creation of a self-learning waste-to-resource database supporting an IS recommendation system by utilizing text analytics techniques. We further demonstrate its practical application to support IS facilitating bodies in their core activity

Enhancing the industrial symbiosis opportunities discovery process

The ‘take-make-use-dispose’ pattern of resource consumption resulting from our modern day economy has continuously pushed the limits of the biosphere, which is expected to provide the required natural resources for production and to assimilate the waste at the end of the consumption life cycle. Foreseeably, the biosphere is unable to support the demands imposed by the linear pattern of economic and anthropogenic activities, with symptomatic effects increasingly being felt through various aspects (e.g. climate change, supply chain disruption, natural resource price surges and volatility, waste accumulation, etc.), which ultimately impacts the society. Consequently, advocates for a more sustainable economy began to call for a re-consideration about the organisation of the economy and its associated patterns of resource usage. Accordingly, the Ellen MacArthur Foundation championed the drive towards a Circular Economy, defining its three key principles of: (1) Design out waste and pollution, (2) Keep products and materials in use, (3) Regenerate natural systems. Supporting one of these key principles is the field of Industrial Symbiosis (IS). The concept of IS encompasses the collaborative efforts amongst a network of business entities that inter-connect with each other on the basis that one entity’s unproductive resources are utilised by another as productive resources. IS is particularly pertinent to Singapore as it is one of the promising avenues to tackle waste problems in Singapore. Waste management has been a perennial challenge to Singapore as it relies on a single offshore island for its landfill requirements. To date, official estimates indicate that this landfill site will only sustain operations for another 16 years. IS alleviates the problem by keeping materials within the economy. To this end, the author’s EngD sponsoring company has commissioned efforts in contributing towards the field of IS. In particular, the aims of this research are to: (1) Study the current industrial practice of IS, (2) Study the support tools promoting IS, (3) Develop an assistive tool to augment IS practitioners’ capabilities, contributing towards fostering IS in Singapore’s context. It is envisioned that by leveraging and weaving in “big data” technologies and platform technologies to develop a customised tool, the goal of bringing IS practices – previously absent – to fruition in the geographical context of Singapore can be achieved. This innovation report highlights the key work conducted over the course of the EngD. Firstly, the study of an industrial leader in facilitated IS – International Synergies Limited – was conducted. The practices and methodology were documented, together with the types of knowledge requirements being analysed and characterised. This sets the foundational knowledge required to replicate IS practices in a new context. Secondly, a systematic literature review was conducted to gather all documented tools that promotes the practice of IS. The outcome of this work provides a historical account of tool development efforts pertinent to the field of IS. From the study, the various tool functions and requirements were discussed in relation to the stages of the IS development process. This study also derived insights to the future development trends in the field and provides a probable trajectory of the features of future IS tools. Finally, based on the gaps identified in the previous two studies, a new tool was implemented that addresses the existing knowledge and information gap. The tool comprises a natural language processing based algorithm which processes raw knowledge sources (e.g. journal articles and patent documents) to extract relational information that can be utilised to assist in the IS matching process. The tool also aims to increase productivity in constructing knowledge bases that is required by the field of IS for recommending matches. A key learning point derived from the set of work undertaken is that while moving towards developing more sophisticated support tools to assist in IS facilitation, one must not lose sight of the larger scope of knowledge requirements to successfully execute a facilitated IS initiative. Adoption of IS requires capabilities that go beyond developing and acquiring the latest enabling technologies, and also includes the capacity building of the more tacit aspect of enabling knowledge, know-hows, acumen and the human touch to gain the required legitimacy and buy-ins from stakeholders for the delivery of an IS programme. Ultimately, by aiming to blur the line differentiating wastes from resources, a higher level of sustainability can be reached through smarter use of resources

A review on chemometric techniques with infrared, Raman and laser-induced breakdown spectroscopy for sorting plastic waste in the recycling industry

Mismanagement of plastic waste globally has resulted in a multitude of environmental issues, which could be tackled by boosting plastic recycling rates. Chemometrics has emerged as a useful tool for boosting plastic recycling rates by automating the plastic sorting and recycling process. This paper will comprehensively review the recent works applying chemometric methods to plastic waste sorting. The review begins by introducing spectroscopic methods and chemometric tools that are commonly used in the plastic chemometrics literature. The spectroscopic methods include near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The chemometric tools include principal component analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS), k-nearest neighbors (k-NN), support vector machines (SVM), random forests (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs) and K-means clustering. This review revealed four main findings. (1) The scope of plastic waste should be expanded in terms of types, contamination and degradation level to mirror the heterogeneous plastic waste received at recycling plants towards understanding potential application in the recycling industry. (2) The use of hybrid spectroscopic method could potentially overcome the limitations of each spectroscopic methods. (3) Develop an open-sourced standardized database of plastic waste spectra would help to further expand the field. (4) There is limited use of more novel machine learning tools such as deep learning for plastic sorting