Power-draw prediction by random forest based on operating parameters for an industrial ball mill

Tohry, A.; Chelgani, S. Chehreh; Matin, S.S.; Noormohammadi, M.

doi:10.1016/j.apt.2019.12.012

Cited by 27 publications

(18 citation statements)

References 21 publications

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“…7 that the MAE of selling expenses prediction is significantly decreased after 300 iterations and gradually tends to converge. 5) and (6). For example, apart from the rule consisted of {(NL, 0.157), (Z, 0.843)} for operating income, {(Z, 0.52), (PL, 0.48)} for total profit, and {(NL, 0.49), (Z, 0.51)} for selling expenses, there is another rule consisted of {(Z, 0.843), (PL, 0.157)} for operating income, {(NL, 0.48), (Z, 0.52)} for total profit, and {(Z, 0.51), (PL, 0.49)} for selling expenses at the division domain D(Z, Z), the resulting new extended belief rule can be calculated and its belief distributions is {(NL, 0.0785), (Z, 0.843), (NL, 0.0785)} in operating income, {(NL, 0.24), (Z, 0.52), (PL, 0.24)} for total profit, and {(NL, 0.245), (Z, 0.51), (PL, 0.245)} for selling expenses.…”

Section: The 1st Part: Data Increment Transformation and Parameter Op...mentioning

confidence: 99%

“…Industrial costs-related studies have attracted the attention of many scholars in recent decade and the research topics mainly focused on the cost-benefit analysis in industrial production [2], cost efficiency of different industries [3], carbon dioxide abatement costs of industries [4] and the low cost of industry wastewater treatment [5]. The scholars also studied on power prediction [6] and energy consumption [7] in industrial production, i.e., Ma et al indicated that predicting a price range is practical and desirable [8]. For the construction of cost prediction model, Chakraborty et al developed a new construction cost prediction model using hybrid natural and light gradient boosting [9]; Jiang et al proposed the cost prediction model for products remanufacturing judgment based on backward propagation artificial neural network [10].…”

Section: Introductionmentioning

confidence: 99%

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Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

Wang

Yang

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Industrial cost is a comprehensive indicator to reflect industrial behaviors, whereas industrial data with annotation are rare because the annotation process is very expensive. Data increment transformation is a feasible solution to enrich annotated industrial data, but it bring a new challenge in system modeling because the size of transformational data is the quadratical relationship with that of collected data, and even turn into big data problem. Hence, a novel rule-based system proposed for handling big data problems, called micro-extended belief rule-based system (Micro-EBRBS), is introduced for industrial cost prediction. Firstly, the Micro-EBRBS is improved by 1) the use of activation factor to revise the calculation of individual matching degrees; 2) the use of parameter optimization to determine the optimal value of basic parameters.Afterwards, on the basis of data increment transformation, a novel industrial cost prediction model, called data incrementbased Micro-EBRBS (DIME) model, is developed to accurately predict industrial costs. In case study, 13 state-own holding industries with historical data from 1999 to 2019 in China are used to illustrate the effectiveness of the DIME model.Comparative results show that the DIME model is more accurate than some existing models in industrial cost prediction.

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Section: The 1st Part: Data Increment Transformation and Parameter Op...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

Wang

Yang

et al. 2022

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

show abstract

“…The random forest algorithm belongs to a form of decision tree algorithm and also represents a kind of ensemble algorithm. It was developed by Leo Breiman Cutler, and because the algorithm has a certain anti-noise ability, its use in the industrial field is more advantageous compared with other algorithms of the same type [7][8][9]. The random forest algorithm is often a combination of multiple different decision tree classifiers, enabling random forests to handle nonlinear data.…”

Section: Random Forest Partmentioning

confidence: 99%

A random forest-neural network coupled model for predicting the recurrence location of uterine cancer

2022

European Journal of Gynaecological Oncology

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With increasing developments and progress, the status and influence of women in society have significantly improved, with more attention paid to women's health. According to relevant statistics, uterine cancer is a highly-incident malignant tumor in females and urges more research to improve the survival rate of uterine cancer patients. In this study, we established a prediction model to determine the location of uterine cancer recurrence by combining random forest and neural network algorithms. Data of uterine cancer patients were collected from major hospitals, and professional doctors evaluated and graded the patients' physical fitness indicators based on their experience, which were then used to construct the model and obtain the prediction results. Compared to traditional method, the proposed method of this paper showed that the model was more effective and accurate in predicting the location of uterine cancer recurrence, with a prediction accuracy rate of up to 88.63%.

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“…[51] There are two steps of randomization: 1) randomly generating bootstrap subsets from variables to develop trees and 2) growing each tree by using split nodes on randomly selected variables. [52] The depth of trees was determined by two parameters: MaxNumSplits and MinLeafSize. The quality of a split was measured by the MSE function.…”

Section: Rf Modelmentioning

confidence: 99%

Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

et al. 2021

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An artificial intelligence approach using machine learning (ML) is applied to predict and optimize the operational performance of the air‐cooled condenser (ACC) in a large‐scale power plant. The in situ data of one whole year are collected from a typical coal‐fired power plant equipped with an ACC. The ML models for predicting the gross power output and the ACC power consumption are established using the artificial neural network (ANN) and random forest (RF) algorithms, and appropriate parameters are determined for the ML models. As the RF models with lower errors are demonstrated to be superior to the ANN models, the RF models are selected. Then the RF models are simplified by identifying the primary input parameters and exploiting fewer input parameters. Based on the derived RF models, the operational performance of the ACC is examined when the total rotational frequency of air fans varies, and the maximum net power output is obtained with the optimal total rotational frequency of fans. Due to the optimization, the average net power output of the plant can be promoted by 0.9774 MW, and the mean of the total rotational frequency of fans increases by 11.40 Hz. Therefore, the proposed approach is beneficial and advantageous.

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Power-draw prediction by random forest based on operating parameters for an industrial ball mill

Cited by 27 publications

References 21 publications

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

Micro-extended belief rule-based system with activation factor and parameter optimization for industrial cost prediction

A random forest-neural network coupled model for predicting the recurrence location of uterine cancer

Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

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