Prediction of Ammonium Removal by Biochar Produced From Agricultural Wastes Using Artificial Neural Networks: Prospects and Bottlenecks

Vu, Ngoc-Thuy; Do, Khac‐Uan

doi:10.1016/b978-0-12-824463-0.00012-4

Cited by 3 publications

(2 citation statements)

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“…ANN has been used to determine the interdependence of input and output variables. With the input database, an appropriate ANN structure can predict output variables [63]. This current study utilized the multilayer perceptrons (MLPs) as the predictive protocol within the ANNs.…”

Section: Predictive Models 252 Artificial Neural Networkmentioning

confidence: 99%

Predictive model for the corrosion inhibition of mild steel in 1.5 M HCl by the leaf-juice of Carica papaya

Inya,

Onyedikachi,

Akuwudike

et al. 2023

Zaštita materijala

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This current study employed the predictive models driven by the artificial neural network (ANN) and multiple regression (MR) to forecast the corrosion inhibition of mild steel in 1.5 M HCl by the leaves of the Carica papaya plant. Corrosion studies were carried out using the weight loss method at room temperature. The corrosion rate of the mild steel coupon, obtained from the study environment was predicted taking into cognizance the influence of the independent variables viz: the time of exposure, the concentration of HCl (1.5 M), and the concentration of the papaya leaf juice. The fresh papaya leaves were obtained within the surrounding of the Federal University of Technology, Owerri, Imo State, Nigeria. A grinding machine powered by an internal combustion engine was used for grinding the fresh leaves, and a white cloth was utilized to squeeze out the juice. Filtration of the produced juice was done twice with a clean white cloth. Different bowls containing mild steel coupons (5.0 x 5.0 x 0.1 cm) and HCl (1.5 M) solutions were treated with Carica papaya leaf extracts at concentrations of 5 ml, 10 ml, 15 ml, and 20 ml. Results indicated that the highest inhibition efficiency of 95.91% was observed after 120 hours when 20 ml of papaya leaf extract was added. Conversely, the highest corrosion rate of 114.19 mpy was recorded after 24 hours of exposure to HCl (1.5 M) with 5 ml of papaya leaf extract. Generally, the addition of papaya leaf extract in different concentrations added to 1.5 M HCl resulted in a decrease in the rate of mild steel corrosion over time. It was noted that the prediction of the experimental corrosion rate by the artificial neural network offered a lesser error in comparison with that obtained by multiple regression according to the error margin obtained after the prediction of the experimental corrosion rate.

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Section: Predictive Models 252 Artificial Neural Networkmentioning

confidence: 99%

Predictive model for the corrosion inhibition of mild steel in 1.5 M HCl by the leaf-juice of Carica papaya

Inya,

Onyedikachi,

Akuwudike

et al. 2023

Zaštita materijala

View full text Add to dashboard Cite

show abstract

“…ANN is considered a sophisticated tool that can carry out a self-learning similar to an ML system. The difference in an ANN system is its ability to carry out self-induction and selfdeduction operations effectively (Yang and Yang, 2014;Vu and Do, 2021). For example, ANN possesses the ability to learn and make intelligent decisions by itself, whereas, for an ML system, the decision-making operations may need to be facilitated by users.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

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The assessment of response surface methodology (RSM) and artificial neural network (ANN) modeling in dry flue gas desulfurization at low temperatures

Makomere

Rutto

Koech

2023

Journal of Environmental Science and Health, Part A

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Prediction of Ammonium Removal by Biochar Produced From Agricultural Wastes Using Artificial Neural Networks: Prospects and Bottlenecks

Cited by 3 publications

References 61 publications

Predictive model for the corrosion inhibition of mild steel in 1.5 M HCl by the leaf-juice of Carica papaya

Predictive model for the corrosion inhibition of mild steel in 1.5 M HCl by the leaf-juice of Carica papaya

Future era of techno-economic analysis: Insights from review

The assessment of response surface methodology (RSM) and artificial neural network (ANN) modeling in dry flue gas desulfurization at low temperatures

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