Modeling and optimization of non-edible papaya seed waste oil synthesis using data mining approaches

Sultana, Nahid; Hossain, S. M. Zakir; Taher, Shaafan; Khan, Amir Muhammad; Razzak, Shaikh A.; Haq, Bashirul

doi:10.1016/j.sajce.2020.07.009

Cited by 16 publications

(5 citation statements)

References 60 publications

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“…Given the relatively few variables in traditional data correlation models, mechanism models for the analysis of the raw material demand do not responding to the process optimization in a timely manner, so the effect is not ideal. Recently, data mining has been widely used in various fields [26,27]. Typically, the task of data mining is to predict variables that are difficult to obtain from experiments.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

Liu

et al. 2021

Processes

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To simultaneously reduce automobile exhaust pollution to the environment and satisfy the demand for high-quality gasoline, the treatment of fluid catalytic cracking (FCC) gasoline is urgently needed to minimize octane number (RON) loss. We presented a new systematic method for determining an optimal operation scheme for minimising RON loss and operational risks. Firstly, many data were collected and preprocessed. Then, grey correlative degree analysis and Pearson correlation analysis were used to reduce the dimensionality, and the major variables with representativeness and independence were selected from the 367 variables. Then, the RON and sulfur (S) content were predicted by multiple nonlinear regression. A multi-objective nonlinear optimization model was established with the maximum reduction in RON loss and minimum operational risk as the objective function. Finally, the optimal operation scheme of the operating variable corresponding to the sample with a RON loss reduction greater than 30% in 325 samples was solved in Python.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

Liu

et al. 2021

Processes

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“…Artificial Neural Networks (ANNs) have been adapted and applied in various applications such as classification, prediction, and recognition due to their structure, which stimulates the brain's biological neural system and provides a strong ability to learn, store, and analyze data [37][38][39]. ANNs consist of multiple layers, including input, output, and hidden layers that generate mathematical models based on prior knowledge.…”

Section: Machine Learning Approach (Narx)mentioning

confidence: 99%

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

et al. 2022

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This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.

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“…32 Several interesting AI applications have been described in various science/engineering study domains. 34,35 However, it is worth noting that the model hyperparameters have a substantial impact on the performance of each AI technique. To obtain the tuned model, it is necessary to optimize these parameters.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence‐based super learner approach for prediction and optimization of biodiesel synthesis—A case of waste utilization

Hossain

Sultana

Irfan

et al. 2022

Intl J of Energy Research

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Modeling and optimization of non-edible papaya seed waste oil synthesis using data mining approaches

Cited by 16 publications

References 60 publications

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

Artificial intelligence‐based super learner approach for prediction and optimization of biodiesel synthesis—A case of waste utilization

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