Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Ni, Du; Xiao, Zhi; Lim, Ming K.

doi:10.1007/s00500-021-05579-7

Cited by 23 publications

(10 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, evidence in support of this position can be found in the work of Panichayakorn and Jermsittiparsert (2019), Zhu et al (2017) and Ni et al (2021), who confirmed that AI and ML can mediate the role of supply chain agility and process improvements. Especially at COVID-19 times, the need of AI and ML-based deep intelligence are seemed to be paramount important to be able to deeply understand the dynamics of demand fluctuation and supply disruption (Di Vaio et al , 2020).…”

Section: Discussionmentioning

confidence: 85%

“…The 15 ML-related publications reported miscellaneous findings, Tables 8 and 9 below summarize the articles and other publications findings, respectively while the discussion section thoroughly elaborates on the same. Most of the ML research specifically focused on demand forecasting, possible back-order prediction and deep learning aspects of demand variability (Abbas et al , 2020; Ni et al , 2021). Risk management, Automated Design and Manufacturing, Supply-Chain Quality Inspection at other areas of ML application among the wider theme of SCM research as our research indicated in Table 9.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Younis

Sundarakani

Alsharairi

2021

JM2

View full text Add to dashboard Cite

Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof. Design/methodology/approach Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM). Findings AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness. Research limitations/implications This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue. Practical implications This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore. Originality/value Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

show abstract

Section: Discussionmentioning

confidence: 85%

Section: Methodsmentioning

confidence: 99%

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Younis

Sundarakani

Alsharairi

2021

JM2

View full text Add to dashboard Cite

show abstract

“…Though ML models are successful in predicting PCR polymer characteristics such as the classification of recycled materials, recycling volume, recycling processing time, and so on, researchers are still unable to predict the properties of PCR polymers. 160 It is implicitly believed that coupling the experiences and perspectives of researchers in the PCR material stream with ML skills and approaches should be able to drive future breakthroughs in predicting the properties of PCR polymers. There is a large research space is out there for further exploration of PCR polymer property predictions using ML models.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

Babu

Curtzwiler

Yun

et al. 2022

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development.

show abstract

“…In traditional target detection algorithms, the target region selection serves to find the region in the input image where the target may appear and get the location of the target to be measured. The sliding window method of selective search is often used, using rectangular boxes of different sizes and aspect ratios, sliding from left to right and from top to bottom to traverse the entire image [18]. Using this exhaustive approach generates a lot of useless and redundant information and is computationally intensive and poorly robust, greatly reducing the speed of the next step of feature extraction and classification.…”

Section: Intelligent Classification and Recyclingmentioning

confidence: 99%

Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology

Chen

2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.

show abstract

Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Abstract: Author post-print (accepted) deposited by Coventry University's Repository 'Machine learning in recycling business: an investigation of its practicality, benefits and future trends',

Cited by 23 publications

References 61 publications

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology

Contact Info

Product

Resources

About