Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

Abdulsalam, Jibril; Lawal, Abiodun Ismail; Setsepu, Ramadimetja Lizah; Onifade, Moshood; Bada, Samson

doi:10.1186/s40643-020-00350-6

Cited by 21 publications

(6 citation statements)

References 55 publications

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“…The ANN learns from a given example and applies the residual knowledge to future prediction. This tool has been used in solving various complex engineering problems as indicated by Lawal and Kwon, 34 Onifade et al, 35 Abdulsalam et al, 37 Akinwekomi and Lawal, 38 etc.…”

Section: Methodsmentioning

confidence: 99%

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

Onifade,

Lawal,

Bada

et al. 2023

ACS Omega

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The temperature at which coal ash melts has a significant impact on the operation of a coal-fired boiler. The coal ash fusion temperature (AFT) is determined by its chemical composition, although the relationship between the two varies. Therefore, it is important to have mathematical models that can reliably predict the coal AFTs when designing coal-based processes based on their coal ash chemistry and proximate analysis. A computational intelligence model based on the interrelationships between coal properties and AFTs was used to predict the AFTs of the coal investigated. A model that integrates the ash, volatile matter, fixed carbon contents, and ash chemistry as input and the AFT [softening temperature, deformation temperature, hemispherical temperature, and flow temperature] as an output provided the best indicators to predict AFTs. The findings from the models indicate (a) a method for determining the AFTs from the coal properties; (b) a reliable technique to calculate the AFTs by varying the proximate analysis; and (c) a better understanding of the impact, significance, and interactions of coal properties regarding the thermal properties of coal ash. This study creates a predictive model that is easy to use, computer-efficient, and highly accurate in predicting coal AFTs based on their ash chemistry and proximate analysis data.

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Section: Methodsmentioning

confidence: 99%

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

Onifade,

Lawal,

Bada

et al. 2023

ACS Omega

Self Cite

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“…The GEP was developed by Ferreira [34] and has gained popularity in most geoengineering publications [35][36][37]. The GEP analyses were performed using GeneXproTools.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Soft computing implementations for evaluating Los Angeles abrasion value of rock aggregates from Kütahya, Turkey

Köken

2024

Acta Tech. Jaurinensis

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The Los Angeles abrasion value (LAAV) of rocks is a critical mechanical aggregate property for designing road infrastructures and concrete quality. However, the determination of this critical aggregate property is labour-intensive and time-consuming and thus, in the literature, there are many predictive models to estimate the LAAV for different rock types. However, most of them are based on classical regression analyses, limiting their broader usage. In this study, several soft computing analyses are performed to develop robust predictive models for the evaluation of LAAV of rocks in the Ilıca region (Kütahya – Turkey). The main motivation for implementing soft computing analyses is that precise predictive models might be useful when exploring suitable rock types that are manufactured in crushing–screening plants. For this purpose, a comprehensive laboratory schedule was established to obtain some inputs for the evaluation of LAAV. As a result of the soft computing analyses, four robust predictive models are developed based on artificial neural networks (ANN), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methodologies. The performance of the proposed models is investigated by some statistical indicators such as R2 and RMSE values and scatter plots. As a result, the ANFIS-based predictive model turns out to be the best alternative to estimate the LAAV of the investigated rocks.

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“…These models were developed using primary data that involved operational parameters (e.g., temperature, residence time, biomass/water ratio) for multiple feedstocks (organic municipal solid waste, agricultural and forestry residue). When compared with ML methods developed with secondary data, predictive performance of MLR models was lower than that of ANN, RT, , and RFR . Among the applications of ML methods, hydrothermal treatment processes were modeled by applying ANN using secondary data, and RFR − using both primary and secondary data.…”

Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

Cited by 21 publications

References 55 publications

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network

Soft computing implementations for evaluating Los Angeles abrasion value of rock aggregates from Kütahya, Turkey

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

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