Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

Kaab, Ali; Sharifi, Mohammad Ali; Mobli, Hossein; Nabavi‐Pelesaraei, Ashkan; Chau, Kwok Wing

doi:10.1016/j.scitotenv.2019.02.004

Cited by 218 publications

(68 citation statements)

References 52 publications

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“…As can be inferred from Table 2, only one document 17 meets the four search criteria, one document 13 covers three search criteria. Most of the documents [61][62][63][64]68,70,73] cover two search criteria, the most common being the conjunction of criteria I and II. Only two documents [71,73] meet a single search criterion.…”

Section: Resultsmentioning

confidence: 99%

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ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra

Heras

Ávila-Gutiérrez

et al. 2020

Sensors

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This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.

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Section: Resultsmentioning

confidence: 99%

“…Environmental assessment of agricultural activities using artificial intelligence techniques has been analyzed jointly with the energy consumption for the cultivation of sugar cane (planted or ratoon farm) [73] or rice [67].…”

Section: Applicationsmentioning

confidence: 99%

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra

Heras

Ávila-Gutiérrez

et al. 2020

Sensors

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“…The data mining technique is also applied in the area of environmental impacts of sugarcane production, for predicting the energy produced and the environmental impacts. Artificial intelligence methods, artificial neural networks, and adaptive neural fuzzy inference system models are also used to predict the environmental impacts of the life cycle and energy output of sugarcane production on planted farms [51].…”

Section: Discussionmentioning

confidence: 99%

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

et al. 2020

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The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.The production of biofuel from sugarcane is supported by the National Agency of Petroleum, Natural Gas and Biofuels' (ANP) sustainable development programs to achieve goals to reduce greenhouse gas emissions. Therefore, annual national targets were developed by the Brazilian National Biofuel Policy (RenovaBio) [12,15]. These goals are established in units of Decarbonization Credits (CBios) calculated from annual mandatory targets defined individually for each producer and distributor according to their participation in the fossil fuel market [12,16,[18][19][20][21].The certification of biofuel production assigns different marks to each biofuel producer and importer, in a value inversely related to the carbon amount of biofuel produced. The note reflects the individual contribution of each producing agent to mitigate a specific amount of GHG concerning its fossil substitute (in tons of CO 2 equivalent). The total emission is compared with that of the equivalent fossil fuel (gasoline, for ethanol), resulting in a final score (Energy-Environmental Efficiency Rating), characterizing the mitigation of emissions. This note generates CBios for biofuel producers and importers; with the decarbonization of the Brazilian energy matrix, there is a mechanism for the commercialization of these CBios linked to the carbon intensity of biofuels [2,12,16,18].The incentive to reduce pollutant emissions in the biofuel chain goes far beyond the use o...

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“…There are three parameters in the DA-RNN: the number of road segments input each time T, the size of hidden states for the encoder m, and the size of hidden states for the decoder p. The optimization method is grid search. The search range of T is (5,10,15,20,25). We set m = p for simplicity.…”

Section: Parameter Settings and Evaluation Metricsmentioning

confidence: 99%

“…In addition to Kalman filtering and SVM, there are other time series prediction methods, such as road segment average travel time [8], the Relevance Vector Machine Regression [9], clustering [10], Queueing Theory combined with Machine Learning [11], and Random Forests [12]. Artificial neural networks have been widely used in various research fields in recent years [13][14][15]. Among artificial neural networks, Multilayer Perceptron (MLP) [16] and Recurrent Neural Network (RNN) [17] have been used to predict bus arrival time.…”

Section: Introductionmentioning

confidence: 99%

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

et al. 2019

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Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

Cited by 218 publications

References 52 publications

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

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