Application of modeling techniques for energy analysis of fruit production systems

Jargan, Hossein; Rohani, Abbas; Kosari-Moghaddam, Armaghan

doi:10.1007/s10668-021-01548-0

Environ Dev Sustain

2021

DOI: 10.1007/s10668-021-01548-0

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Application of modeling techniques for energy analysis of fruit production systems

Hossein Jargan

Abbas Rohani

Armaghan Kosari-Moghaddam

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2024

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Cited by 3 publications

References 39 publications

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Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Nikkhah,

Esmaeilpour,

Kosari‐Moghaddam

et al. 2024

Integr Envir Assess & Manag

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Effective resource allocation in the agri‐food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system. Yet, an essential step in the optimization process is defining the optimization boundaries, or minimum and maximum quantities for the variables. Usually, the boundaries for optimization variables in these studies are obtained from the minimum and maximum values found through interviews and surveys. A deviation in these ranges can impact the final optimization results. To address this issue, this study applies the Delphi method for identifying variable optimization boundaries. A hybrid environmental assessment framework linking LCA, multilayer perceptron artificial neural network, the Delphi method, and genetic algorithm was used for optimizing the pomegranate production system. The results indicated that the suggested framework holds promise for achieving substantial mitigation in environmental impacts (potential reduction of global warming by 46%) within the explored case study. Inclusion of the Delphi method for variable boundary determination brings novelty to the resource allocation optimization process in the agri‐food sector. Integr Environ Assess Manag 2024;20:1759–1769. © 2024 SETAC

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Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Nikkhah,

Esmaeilpour,

Kosari‐Moghaddam

et al. 2024

Integr Envir Assess & Manag

View full text Add to dashboard Cite

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A new methodology for the prediction of optimal conditions for dyes' electrochemical removal; Application of copula function, machine learning, deep learning, and multi-objective optimization

Nabizadeh Chianeh,

Valikhan Anaraki,

Mahmoudian

et al. 2024

Process Safety and Environmental Protection

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Energy Flow Analysis in Oilseed Sunflower Farms and Modeling with Artificial Neural Networks as Compared to Adaptive Neuro-Fuzzy Inference Systems (Case Study: Khoy County)

Nezhad,

Sharabiani,

Tarighi

et al. 2024

Energies

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The evaluation of energy input and output processes in agricultural systems is a crucial method for assessing sustainability levels within these systems. In this research, the investigation focused on the input and output energies and related indices in sunflower farms in Khoy County during the agricultural year 2017–2018. Data were collected from 140 sunflower producers through specialized questionnaires and face-to-face interviews. Additionally, artificial neural networks (ANNs), specifically the multilayer perceptron, were employed to predict the output energy. The results revealed that a substantial portion of the total input energy was attributed to chemical nitrogen fertilizer (43.98%), consumable fuel (25.74%), and machinery (8.42%). The energy efficiency (energy ratio) in these agroecosystems was relatively low, measured at 1.57 for seed and 7.96 for seed and straw. These values should be improved. The energy efficiency in seed production was computed at 0.06 MJ·ha−1, while, for the combined seeds and straw, it was 0.57 MJ·ha−1. In particular, seed energy efficiency represents approximately 11% of the overall biological energy efficiency, highlighting that a substantial 89% of the produced energy is associated with straw. The proper use of this straw is crucial, as its improper handling could lead to a drastic decrease in overall efficiency. Furthermore, the explanatory coefficient (R2) and the mean absolute percentage error (MAPE) to predict the output energy with the best neural network were 0.94, and 1.77 for the training data, 0.97 and 1.55 for the test data, and 0.9 and 2.08 for the validation data, respectively; additionally, 0.97 and 0.42 were obtained by an ANFIS.

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Application of modeling techniques for energy analysis of fruit production systems

Cited by 3 publications

References 39 publications

Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

Machine learning‐based life cycle assessment for environmental sustainability optimization of a food supply chain

A new methodology for the prediction of optimal conditions for dyes' electrochemical removal; Application of copula function, machine learning, deep learning, and multi-objective optimization

Energy Flow Analysis in Oilseed Sunflower Farms and Modeling with Artificial Neural Networks as Compared to Adaptive Neuro-Fuzzy Inference Systems (Case Study: Khoy County)

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