Industrial Symbiosis: Exploring Big-data Approach for Waste Stream Discovery
Bin Songa , Zhiquan Yeoa , Paul Kohlsb , Christoph Herrmannc
a Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, #08-04, Innovis, Singapore 138634
b University of Cincinnati, Cincinnati, Ohio, USA
c Technische Universität Braunschweig, Germany
The 24th CIRP Conference on Life Cycle Engineering
Procedia CIRP 61 ( 2017 ) 353 – 358:
Abstract
Industrial symbiosis is a way to realize circular economy in which waste streams from industrial production and related activities are collected, reused or recycled into resources. The concept is increasingly incorporated into new company design and industrial park development. There also exist waste management companies who collect and process certain waste streams for economic gains. However, industrialized cities typically experience difficulty to increase their total recycling rate due to the lack of complete and detailed data on the types, quantity, and location of waste streams generated, and hence the economically-viable collection and processing of many waste streams. This paper explores and discusses the feasibility and methods for a big data approach to obtain necessary data for discovery of potential industrial symbioses within the perimeter of a large industrialized city. The aim is to realize the objective of embedding industrial symbioses as an essential part of sustainable urban living and management.
. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering.
Keywords: Big Data; Industrial Symbiosis; Waste reuse and recycle; urban sustainability
4. Conclusion
Remarks Industrial symbiosis is an important part of waste reduction for sustainable urban development. However, the complexity, diversity, and dynamics of waste generation sources are a hurdle for gathering a comprehensive set of data for initiating and eventually the operation of industrial symbioses. Leveraging on the widely available data on cyberspace, the big data analytics is deemed a promising approach to identify the potential industrial symbioses within the perimeter of a large industrialized city.
A prerequisite to analyze and discover potential industrial symbioses is to obtain data on the location, type, and quantity of input materials and waste streams cross the concerned geographical region. In this paper, a big data approach is proposed and discussed. The approach consists of three critical steps:
Step 1: searching the internet and webpages for relevant businesses and data;
Step 2: identification of the type of input resources and waste streams; and
Step 3: material quantity estimation.
For step 1, web portals containing classified company data on company category, products/services, and address are found to be better target for first level search. In cases where sufficient information is unavailable, websites of individual companies can be searched. Cross search of a list of web portals, filtering out the duplicated entries, would build up a database containing company name, address, classified category, and product/service type for a geographical region.
A proof-of-concept program using Singapore as the geographical region was developed. The results demonstrated the viability of the approach.
Step 2 employs a modeling approach to correlate product/service type to key business processes, and then to materials input and waste streams. The creation of the product type to processes model and the process to materials model require diverse knowledge of specific business and engineering domains. Fortunately, rich sources of internet portals as well as engineering manuals are found available to extract the required data for the models.
In step 3, company revenue is used to estimate the quantity of required input materials and waste streams. The assumed linear relationships between revenue and amount of input materials and generated waste streams are considered inaccurate, and hence can only provide the initial rough estimation for identifying potential industrial symbioses. However, the results would provide a list of specific potential members of an industrial symbiosis. A focused on-site investigation of the potential members would obtain accurate value for suitability of individual members as well as feasibility assessment of the industrial symbiosis.
The work of this paper is still preliminary. Further research is required to extend the proof-of-concept system to step 2 and 3, to develop a data mining algorithm to extract knowledge for population of the product type to processes model and the process to materials model, and to develop a machine learning algorithm so that collected data sets from reference companies can be used to improve the accuracy of the quantity of input materials and waste streams in reference to a company’s revenue.
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