Computer-aided identification and evaluation of technologies for sustainable carbon capture and utilization using a superstructure approach

Chung, Won‐Suk; Lim, Hyungmuk; Lee, Jeehwan S.; Al-Hunaidy, Ali S.; Ibrahim, Hasan; Jamal, Aqil; Roh, Kosan; Lee, Jay H.

doi:10.1016/j.jcou.2022.102032

Cited by 16 publications

(4 citation statements)

References 52 publications

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“…Technology-based corporate and financial procedures, such as trade credit, can reduce costs, while increasing productivity for all involved parties. As provided, even as the buyer has a stronger credit rating than the seller, revenue management works well [12]. Designing a robust ML-LRA architecture that can handle increasing volumes of unchangeable financial data created from these records has been advocated in light of these problems and the potential for advanced analysis of these documents [13].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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“…The core of this challenge is to optimize decisions related to the capture, separation, transportation and storage/utilization of carbon dioxide. To achieve this goal, scholars have proposed various mathematical models and methods, including Linear Programming (LP), Mixed-Integer Programming (MIP), Mixed-Integer Linear Programming (MILP), Mixed-Integer Non-Linear Programming (MINLP) and Stochastic Mixed-Integer Linear Programming (SMILP) [5][6][7][8][9]. Existing studies typically investigate single or multi-objective CCUS SCND problems in the context of multiple periods, whether deterministic or uncertain.…”

Section: Literature Review On Ccus Supply Chain Network Designmentioning

confidence: 99%

Optimal Design of Resilient Carbon Capture, Utilization and Storage (CCUS) Supply Chain Networks under Facility Disruption

Lei,

Zhang,

Zhu

2024

Sustainability

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In recent years, various kinds of carbon dioxide capture, utilization and storage supply chain network design (CCUS SCND) problems have been extensively studied by scholars from the supply chain management community and other fields. The existing works mainly focus on the various deterministic or uncertainty problems; few works consider the CCUS SCND resilience problem in the context of utilization/storage facility disruptions due to unexpected natural disasters or other geological anomaly events. This paper aims to study the CCUS SCND resilience problem under utilization/storage facility capacity disruption risk. We propose a stochastic mixed-integer linear programming model for the considered problem. In the considered problem, the main decisions related to the following areas are taken into account: supply chain design and planning; facility disruption risk handling, including the optimal determination of facility locations and the matching of carbon dioxide emission sources and utilization/storage facilities; carbon dioxide normal transportation planning; and transshipment planning for various disruption scenarios. Finally, an experimental study comprising a case study from China is conducted to validate the effectiveness and performance of our proposed model. The obtained results show that the supply chain networks for the case study obtained by our proposed model are efficient, cost-effective and resilient in mitigating various kinds of utilization/storage facility disruption scenarios, showing the model can be applied to large-scale CCUS projects to help managers effectively deal with disruption risks. Future research should consider multiple disruption events and propose multiple effective resilience strategies.

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“…Some of them can be solved according to experience, but they cannot solve di cult problems or invention problems, which form obstacles to product or process innovation. Inventing problem-solving theory can help corporate developers solve di cult problems, thereby removing obstacles [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Design Method of Product Concept Model Based on CAD Technology

Meng

2022

Scientific Programming

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With the comprehensive development of information technology, the continuous optimization of products can make enterprises stand in the market. Various industries are involved in the upsurge of computer-enabling products; computer-aided (CAD) technology has also been rapid development. CAD technology is composed of problem analysis, innovative method, problem transformation and problem-solving invention principle, standard solution, and so on. In this paper, the theoretical system, basic scheme, and solving algorithm of CAD technology are described in detail. Meanwhile, the content of value evaluation and root cause analysis is analyzed and solved to further illustrate the application process of CAD technology optimization products and gradually form a more perfect application method of CAD innovation technology so that its solution process is more scientific, operable, and universal.

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Computer-aided identification and evaluation of technologies for sustainable carbon capture and utilization using a superstructure approach

Cited by 16 publications

References 52 publications

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Optimal Design of Resilient Carbon Capture, Utilization and Storage (CCUS) Supply Chain Networks under Facility Disruption

Design Method of Product Concept Model Based on CAD Technology

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