An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO
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            emission

Nguyen, Van Giao; Duong, Xuan Quang; Nguyen, Lan Huong; Nguyen, Phuoc Quy Phong; J, Priya; Truong, Thanh Hai; Le, Huu Cuong; Pham, Nguyen Dang Khoa; Nguyen, Xuan Phuong

doi:10.1080/15567036.2023.2231898

Cited by 14 publications

(3 citation statements)

References 149 publications

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“…It is noticeable that IoT, Artificial Intelligence (AI), machine learning (ML), real-time monitoring, and optimization techniques are considered some of the emerging practices that are reshaping the world and how research is performed. As mentioned, most of the methodologies mentioned herein are based on the calculation of CO 2 emission quantity by empirical methods, but Van Gio et al [51] performed an extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emissions. They show that predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO 2 ) emissions in innovating ways to understand CO 2 emission.…”

Section: Literature Review Of Cement Co 2 Emission Calculation and Pr...mentioning

confidence: 99%

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Boakye,

Fenton,

Simske

2023

JMMP

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This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO2 during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO2 emissions in a cement-producing facility. Due to the intricacy of the calcination process, it has historically been challenging to precisely anticipate the CO2 produced. The purpose of this study is to determine a direct association between CO2 generation from the manufacture of raw materials and the process factors. In this paper, six machine learning techniques are investigated to explore two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. CO2 molecular composition (dry basis) sensitivity analysis uses over 6000 historical manufacturing health data points as input variables, and the results are used to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models is examined, and the models are then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable has the biggest impact on CO2 emissions, the significance of the other factors is also assessed.

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Section: Literature Review Of Cement Co 2 Emission Calculation and Pr...mentioning

confidence: 99%

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Boakye,

Fenton,

Simske

2023

JMMP

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“…By emphasizing climate action and environmental sustainability, net-zero targets, which aim to achieve a balance between the removal of greenhouse gases and the emissions of those gases, are closely aligned with these aims [6], [7]. Machine learning is critical in accelerating progress toward the SDGs and net-zero objectives [8], [9]. Machine learning can analyze massive volumes of data and recognize complex patterns [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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“…According to data from 2016, emissions in transportation activities account for about one-quarter of total emissions; more specifically, CO 2 emissions are currently about 8 Gt, representing an increase of about 71% compared to 1990 [10]. Among these, emissions from freight activities account for 42% of all transportation emissions [11][12] [13], and are expected to reach 60% by 2050 [14]. Moreover, the remarkable decline of NOx, PM, and CO 2 emissions during lockdown due to the COVID-19 pandemic could be considered as believable proof of the significant contribution of transport activities to pollutant emissions [15][16] [17].…”

Section: Introductionmentioning

confidence: 99%

Role of Green Logistics in the Construction of Sustainable Supply Chains

Pham,

Dinh,

Pham

et al. 2023

Polish Maritime Research

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The global supply chain has been growing strongly in recent years. This development brings many benefits to the economy, society, and human resources in each country but also causes a large number of concerns related to the environment since traditional logistics activities in the supply chain have been releasing a significant amount of emissions. For that reason, many solutions have been proposed to deal with these environmental pollution problems. Among these, three promising solutions are expected to completely solve environmental problems in every supply chain: (i) Application of blockchain in the supply chain, (ii) Use of renewable energy and alternative fuels, and (iii) Design of a closed supply chain. However, it seems to lack a comprehensive study of these solutions aiming to overcome the drawbacks of traditional logistics. Indeed, this work focuses on analyzing and evaluating the three above-mentioned solutions and the impacts of each solution on solving problems related to traditional logistics. More importantly, this work also identifies critical factors and challenges such as policies, laws, awareness, and risks that are found to be remarkable difficulties in the shifting progress of traditional logistics to green logistics. Finally, directions for developing and deploying green solutions to the logistics, supply chain, and shipping sectors toward decarbonization strategies and net-zero goals are discussed in detail.

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An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO ₂ emission

Cited by 14 publications

References 149 publications

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Role of Green Logistics in the Construction of Sustainable Supply Chains

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An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emission

Cited by 14 publications

References 149 publications

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Role of Green Logistics in the Construction of Sustainable Supply Chains

Contact Info

Product

Resources

About

An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO ₂ emission