AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations DOI Open Access
Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

и другие.

Sustainability, Год журнала: 2024, Номер 16(23), С. 10358 - 10358

Опубликована: Ноя. 27, 2024

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, electric vehicles. The primary objective is to quantify the impact of practices on resource efficiency environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using Brazilian Land-Use Model for Energy Scenarios (BLUES) Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction optimization. Significant findings include a 20–25% waste production an improvement recycling from 50% 83% over decade. Predictive demonstrated high accuracy, less than 5% deviation actual performance metrics, supported by error metrics such Mean Absolute Percentage Error (MAPE) Root Square (RMSE). Statistical validations confirm reliability these forecasts. highlights potential reduce reliance virgin materials lower carbon while emphasizing critical role policy support technological innovation. integrated offers actionable insights industries seeking sustainable growth, providing robust framework future management applications.

Язык: Английский

AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations DOI Open Access
Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

и другие.

Sustainability, Год журнала: 2024, Номер 16(23), С. 10358 - 10358

Опубликована: Ноя. 27, 2024

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, electric vehicles. The primary objective is to quantify the impact of practices on resource efficiency environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using Brazilian Land-Use Model for Energy Scenarios (BLUES) Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction optimization. Significant findings include a 20–25% waste production an improvement recycling from 50% 83% over decade. Predictive demonstrated high accuracy, less than 5% deviation actual performance metrics, supported by error metrics such Mean Absolute Percentage Error (MAPE) Root Square (RMSE). Statistical validations confirm reliability these forecasts. highlights potential reduce reliance virgin materials lower carbon while emphasizing critical role policy support technological innovation. integrated offers actionable insights industries seeking sustainable growth, providing robust framework future management applications.

Язык: Английский

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