Multi criteria decision analysis for screening carbon dioxide conversion products

Pacheco, Kelvin A.; Bresciani, Antônio E.; Alves, Rita M.B.

doi:10.1016/j.jcou.2020.101391

Cited by 22 publications

(11 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods, TOPSIS, and Shannon entropy weight elicitation have been exploited by researchers to select appropriate product [31]. Apart from the technology-based solutions, CO 2 emission can be reduced by applying natural solutions, such to decreasing number of flights, use of efficient driving techniques, plantation move, transformation to clean energy, eating less red meat, reducing or even making efficient use of the energy consumption at homes, and many other such solutions [32].…”

Section: Co 2 Emissionmentioning

confidence: 99%

“…In this research, we have used Weka 3.8 Machine Learning library as a simulation tool on a standalone Intel(R) Core(TM) i5-8250U CPU @ 1.60 GHz 1.80 GHz system having 8 GB internal memory. e CO 2 prediction model is developed by adopting exploratory data analysis (EDA) [31] of the Weka library. For the estimation of realistic weights/importance score of the features, we have adopted Saaty's method [27], implemented in Sanna Excel plugin that uses AHP method [37,38].…”

Section: Experimental Environment and Setupmentioning

confidence: 99%

See 1 more Smart Citation

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Ali

Iqbal

Zada

2022

Scientific Programming

View full text Add to dashboard Cite

The fast industrial revolution all over the world has increased emission of carbon dioxide (CO2), which has badly affected the atmosphere. Main sources of CO2 emission include vehicles and factories, which use oil, gas, and coal. Similarly, due to the increased mobility of automobiles, CO2 emission increases day-by-day. Roughly, 40% of the world’s total CO2 emission is due to the use of personal cars on busy and congested roads, which burn more fuel. In addition to this, the unavailability of parking in all parts of the cities and the use of conventional methods for searching parking areas have added more to this problem. To solve the problem of reducing CO2 emission, a novel cloud-based smart parking methodology is proposed. This methodology enables drivers to automatically search for nearest parking(s) and recommend the most preferred ones that have empty lots. For determining preferences, the methodology uses the analytical hierarchy process (AHP) of multicriteria decision-making methods. For aggregating the decisions, the weighted sum model (WSM) is adopted. The methods of sorting, multilevel multifeatures filtering, exploratory data analysis (EDA), and weighted sum model (WSM) are used for ranking parking areas and recommending top-k parking to the drivers for parking their cars. To implement the methodology, a scenario comprising cars, smart parkings are considered. To use EDA, a freely available dataset “2020testcar-2020-03-03” is used for the estimation of CO2 emitted by cars. For evaluation purpose, the results obtained are compared with the results of traditional approach. The comparison results show that the proposed methodology outperforms the traditional approach.

show abstract

Section: Co 2 Emissionmentioning

confidence: 99%

Section: Experimental Environment and Setupmentioning

confidence: 99%

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Ali

Iqbal

Zada

2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…33 Due to its user friendly interface and high precision, it has been employed in many different applications over the years, such as in the selection of ideal solutions for the reduction in net carbon emissions, 34 selection of optimal technology for Power-to-X system (transformation of municipal waste to energy), 35 formulating sustainable fertilizer for oil palm plantations, 36 and optimization for the conversion of CO 2 to high-value products. 37 Both the assessment variables (i), H 2 yield, and CO 2 reduction from each output, at each operating condition (P i,LHC ) are normalized (P Norm i,LHC ) to the scale of 0 and 1 in accordance with each of the LHC inputs. Here, P þ i,LHC and P À i,LHC represent the positive-ideal and negative-ideal values reported from the results of catalytic dry LHC reforming, as shown in Equation (1).…”

Section: Topsis Selection For Optimal Lhcmentioning

confidence: 99%

Machine learning–assisted CO ₂ utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

Lim

Loy

Alhazmi

et al. 2021

Intl J of Energy Research

View full text Add to dashboard Cite

The catalytic dry reforming (DR) process is a clean approach to transform CO 2 into H 2 and CO-rich synthetic gas that can be used for various energy applications such as Fischer-Tropsch fuels production. A novel framework is proposed to determine the optimum reaction configurations and reaction pathways for DR of C 1 -C 4 hydrocarbons via a reaction mechanism generator (RMG). With the aid of machine learning, the variation of thermodynamic and microkinetic parameters based on different reaction temperatures, pressures, CH 4 /CO 2 ratios and catalytic surface, Pt(111), and Ni(111), were successfully elucidated. As a result, a promising multicriteria decision-making process, TOPSIS, was employed to identify the optimum reaction configuration with the trade-off between H 2 yield and CO 2 reduction. Notably, the optimum conditions for the DR of C 1 and C 2 hydrocarbons were 800 C at 3 atm on Pt(111); whereas C 3 and C 4 hydrocarbons found favor at 800 C and 2 atm on Ni(111) to attain the highest H 2 yield and CO 2 conversion. Based on the RMG-Cat (first-principle microkinetic database), the energy profile of the most selective reaction pathway network for the DR of CH 4 on Pt(111) at 3 atm and 800 C was deducted. The activation energy (E a ) for C H bond dissociation via dehydrogenation on the Pt(111) was found to be 0.60 eV, lower than that reported previously for Ni(111), Cu(111), and Co(111) surfaces. The most endothermic reaction of the CH 4 reforming process was found to be C 3 H 3 * + H 2 O* $ OH* + C 3 H 4 (218.74 kJ/mol).

show abstract

“…In order to analyse the degree of importance of each CO2 valorisation option (Methanol production, Cement production, Algae ponds / Greenhouses, Food and Beverage production), a multi-criteria decision analysis was carried out. Choosing the right analysis method, TOPSIS was chosen as the most appropriate [22], [23].…”

Section: Multi-criteria Analysismentioning

confidence: 99%

Analysis of CO₂ Valorisation Options for Regional Development

Terjanika

Gusca

Pubule

et al. 2021

Environmental and Climate Technologies

View full text Add to dashboard Cite

Mitigation of CO2 emissions has become a top-question in international and national arenas, likewise on the city level. Existing CO2 mitigation measures are primarily oriented towards wider deployment of low-carbon technologies of renewable energy sources and energy efficiency measures, focusing on energy production, distribution and energy use sectors, as well as the transport sector. Due to higher CO2 reduction efficiency and the cost aspect, the direct CO2 mitigation options currently applied are mostly oriented on large scale CO2 generators. Meanwhile the rural, sparsely populated regions already suffer from a lack of innovative industrial economic activities, inhabitant’s migration to urban areas and mostly involved in agriculture, land use and forestry activities. They are also “saved” by the public authorities from targeted CO2 emissions mitigation actions, therefore, the understanding of processes within rural CO2 economy sectors, factors, interconnections and effects to the environment and nature quality and finally guidelines to future actions are crucial. To analyse CO2 valorisation options for regional development, a multi-modelling approach combining literature review, an indicator analysis method and a multi-criteria decision-making analysis were used. As a result, CO2 valorisation options and key performance indicators were defined and multi-criteria analysis for regional decarbonization scenarios were performed.

show abstract

Multi criteria decision analysis for screening carbon dioxide conversion products

Cited by 22 publications

References 55 publications

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Machine learning–assisted CO ₂ utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

Analysis of CO₂ Valorisation Options for Regional Development

Contact Info

Product

Resources

About

Multi criteria decision analysis for screening carbon dioxide conversion products

Cited by 22 publications

References 55 publications

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Multicriteria Decision Making for Carbon Dioxide (CO2) Emission Reduction

Machine learning–assisted CO 2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

Analysis of CO2 Valorisation Options for Regional Development

Contact Info

Product

Resources

About

Machine learning–assisted CO ₂ utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses

Analysis of CO₂ Valorisation Options for Regional Development