Comparative analysis of adaptive neuro-fuzzy inference system (ANFIS) and RSRM models to predict DBP (trihalomethanes) levels in the water treatment plant

Okoji, Comfort N.; Okoji, Anthony I.; Ibrahim, Musa S.; Okpoko, Valentine Obinna

doi:10.1016/j.arabjc.2022.103794

Cited by 18 publications

(6 citation statements)

References 44 publications

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“…For its predictive purposes, ANFIS currently uses an advanced rapid-learning technique called hybrid learning. It has been established by many scientists that hybrid algorithms are effective [39,40].…”

Section: Anfis Model Buildingmentioning

confidence: 99%

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

Okoji,

Taiwo,

Adeoye

et al. 2024

International Journal of Energy Research

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An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification of biomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition was characterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is used to examine the effects of the significant input variables on response variables, such as hydrogen mole fraction and hydrogen energy content. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters were considered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature, while the gasification temperature has an optimum value. ANFIS was used to predict hydrogen of mole fraction, 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure and temperature of 1 bar and 848°C, respectively. With the steam-gasification model operating at temperature (850°C), pressure (1 bar), and S/B ratio of 2.0, an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen, while RSM gave an increase of 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactor pressure and temperature of 1 bar and 850°C, respectively. Varying the gasifier temperature and S/B ratio have, on the other hand, a crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation, prediction, and optimization to increase a significant impact on the products especially hydrogen.

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Section: Anfis Model Buildingmentioning

confidence: 99%

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

Okoji,

Taiwo,

Adeoye

et al. 2024

International Journal of Energy Research

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“…The current method is highly reliable for the identification of the characteristics of effluent. Okoji et al (2022) used this model for the prediction and the removal of the trihalomethanes and removed successfully from the influent. Mullai et al (2022) used similar methods for modelling the sludge blanket reactor to treat the industrial effluents.…”

Section: Anln-fsmentioning

confidence: 99%

“…Dobosz et al, 2017;Okoji et al, 2022). The conventional polymeric ultrafiltration membranes are fabricated using the phase inversion method and have limitations like the lower flux and high fouling rate.…”

mentioning

confidence: 99%

A proposed Fuzzy logic model for Electrospun nanofiber pressurised membranes for water treatment- A review

Rao¹,

Kumari²,

Karnena

2023

JANS

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Electrospun nanofiber membranes and nanocomposites for water treatment is an emerging concept growing faster in science, gaining more prominence among various scientists, and creating attention to improve water quality. The familiarity with fabricating these materials is increased, and researchers worldwide target more nanomaterial manufacturing and synthesis for several applications. The electrospinning process for nanofibers' preparation allows polymers to incorporate various functionalized materials. Scientists have recently proposed electrospun membranes using fluorinated compounds and polymers with hydrocarbons. The membranes prepared by the electrospun nanofiber to purify water created a primary research axis, and various laboratory experiments proved efficient. In recent years, the usage of fuzzy analysis in industries has increased, and these logics are applied in every unit process of the water industry. The present review primarily discusses the recent pressurized electrospun membrane technologies utilized in the water industries and machine learning methods that can be applied in the water industries. Further, it suggests a new novel way of integrating options to connect the fuzzy with water treatment to enhance the electrospun nanofiber prospects in the water industry. Implementing these fuzzy models in future by water industries might reduce maintenance costs and help in understanding the characteristics of the effluent quality in detail.

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“…There is an idea about the injection of chlorine in WTPs and the formation of tri halo methane (THM) as a carcinogenic compound [53] that should be controlled. However, cyanide has an acute effect in crisis conditions and THM has a chronic one.…”

Section: Ai and Soft-sensor Designmentioning

confidence: 99%

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

et al. 2022

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Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments.

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Comparative analysis of adaptive neuro-fuzzy inference system (ANFIS) and RSRM models to predict DBP (trihalomethanes) levels in the water treatment plant

Cited by 18 publications

References 44 publications

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

A proposed Fuzzy logic model for Electrospun nanofiber pressurised membranes for water treatment- A review

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

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