Environmental emissions—the concern for the decade

Tony Hadfield

doi:10.1049/pe:19930011

Multi-objective optimization of energy use and environmental emissions for walnut production using imperialist competitive algorithm

Applied Energy ◽

10.1016/j.apenergy.2020.116342 ◽

2020 ◽

pp. 116342

Author(s):

Majid Khanali ◽

Asadollah Akram ◽

Javad Behzadi ◽

Fatemeh Mostashari-Rad ◽

Zahra Saber ◽

...

Keyword(s):

Energy Use ◽

Imperialist Competitive Algorithm ◽

Multi Objective Optimization ◽

Multi Objective ◽

Competitive Algorithm ◽

Environmental Emissions

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Effect of carbon anode production parameters on anode cracking

SN Applied Sciences ◽

10.1007/s42452-020-04133-8 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Salah Amrani ◽

Duygu Kocaefe ◽

Yasar Kocaefe ◽

Dipankar Bhattacharyay ◽

Mohamed Bouazara ◽

...

Keyword(s):

Electrical Resistivity ◽

Crack Density ◽

Raw Material ◽

Carbon Anode ◽

Know How ◽

Production Parameters ◽

Environmental Emissions ◽

Carbon Anodes ◽

Tomographic Analysis

AbstractCarbon anodes are used in the electrolytic production of aluminum. The quality of anodes is directly related to the production cost, carbon and energy consumption, and environmental emissions. It is desired that the anodes have high density, low porosity/cracks, low electrical resistivity as well as low air and CO2 reactivities. Low resistivity of anodes reduces energy required to produce aluminum during electrolysis. The presence of cracks and pores increases the anode electrical resistivity. Therefore, it is important to know how and when the pores and cracks form during the anode production so that the necessary actions could be taken to prevent their formation. A study was carried out to investigate the effect of different anode production parameters such as anode composition, type of raw material used, time and top-former bellow pressure of vibro-compactor, green anode cooling medium, and heating rate used during baking on the crack formation. The anodes are fabricated at the carbon laboratory of University of Quebec at Chicoutimi (UQAC) and characterized by measuring their properties (density, electrical resistivity, and surface crack density). The anode properties, hence the anode quality, were correlated with the anode production parameters. Also, their tomographic analysis was carried out to visualize and quantify the internal cracks. Graphical abstract

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Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

Sustainability ◽

10.3390/su13010290 ◽

2020 ◽

Vol 13 (1) ◽

pp. 290

Author(s):

Seyed Hashem Mousavi-Avval ◽

Shahin Rafiee ◽

Ali Mohammadi

Keyword(s):

Genetic Algorithm ◽

Life Cycle ◽

Fuzzy Inference ◽

Cost Ratio ◽

Inference System ◽

Benefit Cost Ratio ◽

Multi Objective Genetic Algorithm ◽

Neuro Fuzzy ◽

Environmental Emissions ◽

Benefit Cost

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

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