Advanced Multiresponse Process Optimisation

Šibalija, Tatjana V.; Majstorovic, Vidosav D.

doi:10.1007/978-3-319-19255-0

Cited by 20 publications

(23 citation statements)

References 91 publications

(164 reference statements)

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“…The data for the 5092 cases generated by analysis of the operational characteristics for excavators was divided into two parts: the training set and the testing set. There are no accepted mathematical rules for determining the size of the dataset to be used for training and testing, and the number of training cycles or iterations is almost always decided on by rule of thumb, based on experience and trial-and-error, in order to reach a minimum percentage value of mean square errors [35,63]. Therefore, trials with various sizes of training and testing databases, created from the whole database, were carried out using 75%-93% and 25%-7%, respectively, of the data for the training and testing database subsets in both ANN models (see Tables A1 and A2).…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

“…This was based on the minimum value of mean square error for the training data subset, which is considered the essential criterion to represent the best performance for the backward propagation learning method used for the ANN model adopted to select the best combination [63]. In addition, the value correlation coefficient (R) is considered more useful for comparing appropriate models with the different number of predictors for ANNs [58].…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

“…In addition, the value correlation coefficient (R) is considered more useful for comparing appropriate models with the different number of predictors for ANNs [58]. R also represents the correlation value between the prediction and the actual output value of a neural network [63]. However, R is a poor measure when it is zero or near zero (which indicates a lack of a relationship between the predicted and actual output of the ANN model) [63].…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

“…R also represents the correlation value between the prediction and the actual output value of a neural network [63]. However, R is a poor measure when it is zero or near zero (which indicates a lack of a relationship between the predicted and actual output of the ANN model) [63]. Thus, a value for R of 1 (or near 1) is seen as a robust indicator of good relevance between the predicted and the actual output [63].…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

“…However, R is a poor measure when it is zero or near zero (which indicates a lack of a relationship between the predicted and actual output of the ANN model) [63]. Thus, a value for R of 1 (or near 1) is seen as a robust indicator of good relevance between the predicted and the actual output [63]. Therefore, 0.9 is a minimum value of R in order for the neural network model to be considered a good model, one that represents a perfect fit between the target and actual output [63].…”

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

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Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Jassim

Olofsson

2017

Sustainability

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Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO 2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO 2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator's hourly energy consumption and CO 2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook's data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO 2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO 2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO 2 emissions.

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Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

Section: Designing the Predictive Ann Model With Forwards/backwards Pmentioning

confidence: 99%

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Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Jassim

Olofsson

2017

Sustainability

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Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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Value-added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value-added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre-treatment processes (microwave-assisted, organosolv-, ultrasound-assisted pre-treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids.

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NSGA III for CNC End Milling Process Optimization

Ghosh

Martinsen

2020

Communications in Computer and Information Science

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Computer Numerical Controlled (CNC) end milling processes require very complex and expensive experimentations or simulations to measure the overall performance due to the involvement of many process parameters. Such problems are computationally expensive, which could be efficiently solved using surrogate driven evolutionary optimization algorithms. An attempt is made in this paper to use such technique for the end milling process optimization of aluminium block and solved using Non-dominated Sorting Genetic Algorithm (NSGA III). The material removal rate, and surface roughness are considered as the crucial performance criteria. It is shown that the regression driven NSGA III is efficient and effective while obtaining improved process responses for the end milling.

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Advanced Multiresponse Process Optimisation

Cited by 20 publications

References 91 publications

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Artificial neural networks in valorization process modeling of lignocellulosic biomass

NSGA III for CNC End Milling Process Optimization

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