Adaptive decision model of product team organization pattern for extracting new energy from agricultural waste

Zheng, Jiangbo; Wu, Zhixin; Sharma, Rohit; Lv, Haibin

doi:10.1016/j.seta.2022.102352

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…Soil erosion, depletion of water tables, reduction of biodiversity at the ecosystem level, and degradation of genetic variation in the species of cultivated plants can all result from the oversimplification of agroecosystems through the widespread adoption of monocultures (Behroozeh et al, 2022). With the measurement of land productivity, the output energy to input energy ratio becomes an environmental loading indicator (Zheng et al, 2022). The intensity with which agricultural practices impact natural ecological processes can be measured, for example, by measuring the amount of exosmotic energy applied per hectare of agroecosystem (Koondhar et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Comparing the effects of agricultural intensification on CO2 emissions and energy consumption in developing and developed countries

Khan

Alabsi

Muda

2023

Front. Environ. Sci.

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Energy consumption has become a requirement in the modern world, and without it, the economies of developing nations cannot prosper. Consistent economic growth is a challenge for countries of all economic levels, not just the less developed ones. We test the EKC hypothesis by analyzing the relationships between GDP growth, energy consumption, agricultural output, and the consequences of carbon dioxide (CO2) emissions. From 1991 to 2016, we used panel and quantile regression analysis to compare emissions in nine developing countries with those in 13 developed countries. There is the beginning of a reverse U-shaped relationship between agricultural energy use and greenhouse gas emissions. As a result, the verified EKC hypothesis paves the way for a watershed moment in the progress of industrialized nations’ economies. As an added bonus, agricultural results have a positive impact on CO2 emissions from using liquid fuels. It has a negative impact on CO2 emissions by 19.12% and causes a 4.802 percent increase in environmental degradation. Feed cropping, deforestation, biomass burning, and deep soil and cropping also have negative effects on the environment, especially in developing countries. There is a negative correlation between CO2 emissions and economic growth in developing countries and their energy consumption. Although the EKC hypothesis for CO2 emissions was rejected at lower quantiles, it was validated for Qatar, Canada, China, and other high-emitting economies according to the empirical estimation of quantile regression. The findings of this study have important policy implications for reducing carbon dioxide emissions, suggesting that policymakers account for the stage of economic growth currently being experienced when formulating measures to cut energy use and protect the environment. In particular, policies aimed at reducing energy consumption could.

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Section: Discussionmentioning

confidence: 99%

Comparing the effects of agricultural intensification on CO2 emissions and energy consumption in developing and developed countries

Khan

Alabsi

Muda

2023

Front. Environ. Sci.

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“…Zheng et al [18] aim to resolve the low efficiency while extracting novel energy from agricultural waste (AW). The consumption and development of AW were deeply elaborated upon by merging the recent technological progression.…”

Section: Related Workmentioning

confidence: 99%

“…The modification in the place of the sailfish operation can be performed by taking free space on the prey farm or by changing the attack technique. The transition of Sailfish is focused mostly on elite and injured sardine locations demonstrated in Equation (18).…”

Section: Sailfish Position Updatingmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

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Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.

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“…1,2 Therefore, the development and utilization of renewable energies to replace traditional fossil energies have caught much attention. 3 Biomass energy, 4 as an ideal chemical with a short regeneration cycle containing large energy, is significantly promising. [5][6][7][8][9][10] The catalytic conversion of biomass platform molecules, such as furfural (FAL), is one of the best choices to generate high-valued chemicals.…”

Section: Introductionmentioning

confidence: 99%

Photocatalytic reduction of biomass‐derived aldehydes over Pt‐loaded on NiIn bimetallic oxides under light‐emitting diode lighting at mild conditions

Zhang,

He,

Miao

et al. 2024

Applied Organom Chemis

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Photocatalytic conversion of renewable biomass‐derived aldehydes into high‐valued chemicals is a promising path to meet the growing demand for clean energy and chemical resources. However, the approach still faces challenges, such as limited reaction types and low utilization rates due to the complex functional groups and involved polytropic reaction. In the present work, we developed a catalyst of Pt nanoparticles (Pt NPs) supported on a NiO–In2O3 bimetallic oxide (denoted as Pt/NiInOx) for photocatalytic hydrogenation of furfural (FAL) to furfural alcohol (FOL). Owing to the excellent band structure and efficient internal charge transfer on the p–n heterojunction in the catalyst, the highest 99.9% yield to FOL was obtained over the Pt/NiInOx under a light‐emitting diode (LED) light at low temperatures as 273 K. The combination of ·H dissociated from H2 and oxygen vacancy work synergically to adsorb, activate, and converse C=O to the C–O group. The photocatalytic system presents a highly efficient approach for the rapid hydrogenation of biomass‐based aldehydes under a low LED light radiation intensity at a low temperature. The work is expected to serve as an important reference in constructing bimetallic oxide‐supported active metals, enabling efficient separation of photogenerated electrons and holes, and offering oxygen vacancies in various photocatalytic reactions.

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Adaptive decision model of product team organization pattern for extracting new energy from agricultural waste

Cited by 9 publications

References 32 publications

Comparing the effects of agricultural intensification on CO2 emissions and energy consumption in developing and developed countries

Comparing the effects of agricultural intensification on CO2 emissions and energy consumption in developing and developed countries

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Photocatalytic reduction of biomass‐derived aldehydes over Pt‐loaded on NiIn bimetallic oxides under light‐emitting diode lighting at mild conditions

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