Application of data-driven algorithms for the forecasting of non-linear parameter

Abba, S.I.; Maihula, A S; Jibril, M. B.; Sunusi, A M; Ahmad, M. Ali; Saleh, M.A.

doi:10.14445/23497157/ijres-v6i2p103

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The methods of reducing emissions of harmful substances by influencing the working process include the improvement of the fuel supply process [3,4,5]. This is due to the fact that the processes of fuel supply, atomization and mixture formation predetermine the nature of the subsequent combustion process and, as a result, the mentioned environmental indicators of a diesel engine.…”

Section: Introductionmentioning

confidence: 99%

Regulation of the fuel injection advance angle in a transport diesel engine

Markov,

Furman,

Plahov

et al. 2023

J. Phys.: Conf. Ser.

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The toxicity indicators of exhaust gases of a diesel engine largely depend on the quality of the fuel supply process. The possibility of a significant improvement in the toxicity of the exhaust gases of a diesel engine by optimizing the phase of the start of fuel supply - the fuel injection advance angle is shown. To assess the influence of the fuel injection advance angle on the dynamic performance of the engine and the toxicity of exhaust gases, a mathematical model of the engine has been developed. The model contains differential equations for the main elements of the combined engine - its piston part, turbocharger, in take and exhaust pipelines. These equations include polynomial dependences of the main engine parameters on its operating parameters. The object of research was diesel type D-245 (4 ChN 11/12.5) produced by the Minsk Motor Plant. Calculations of the acceleration of the engine with a variable fuel injection advance angle have been carried out. The influence of the law of regulation of the advance angle of fuel injection on the duration of the transient process and emissions of normalized toxic components is determined.

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Section: Introductionmentioning

confidence: 99%

Regulation of the fuel injection advance angle in a transport diesel engine

Markov,

Furman,

Plahov

et al. 2023

J. Phys.: Conf. Ser.

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“…(Abba et al, , 2019Abba, Hadi, et al, 2020;Abba, Linh, et al, 2020;Abba & Elkiran, 2017;Abdullahi et al, 2020;Elkiran et al, 2018Elkiran et al, , 2019Gaya et al, 2020;Hadi et al, 2019;M A et al, 2019;Q. B. Pham et al, 2019;S I et al, 2019;Usman et al, 2020;Yaseen et al, 2018Yaseen et al, , 2019Yaseen et al, , 2020). Here's a glance at some research that used ML for predicting the CS of various concretes.…”

Section: Introductionmentioning

confidence: 99%

High-strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

Jibril

Malami²,

Muhammad

et al. 2023

Preprint

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The most crucial mechanical property of concrete is compression strength (CS). Insufficient compressive strength can therefore result in severe failure and is very difficult to fix. Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-Strength Concrete (HSC) is an extremely complicated material, making it challenging to simulate its behaviour. The CS of HSC was predicted in this research using an Adaptive Neuro-fuzzy Inference system (ANFIS), Backpropagation neural networks (BPNN), Gaussian Process Regression (GPR), and NARX neural network (NARX) In the initial case, whereas in the second case, an ensemble model of k-Nearest Neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX and M1 in GPR. The output variable is the 28-day CS (MP) and the input variables are cement (Ce) Kg/m3, water (W) Kg/m3, superplasticizer (S) Kg/m3, coarse aggregate (CA) Kg/m3, and Fine aggregate (FA) Kg/m3. The outcomes depict that the suggested approach is predictively consistent for forecasting the CS of HSC, to sum up. The MATLAB 2019a toolkit was employed to generate the MLs learning models (ANFIS, BPNN, GPR, and NARX), whereas E-Views 11.0 was used for pre-and post-processing of the data, respectively. The model for BPNN and NARX modelling was trained and validated using MATLAB code. The outcome depicts that, the Combination M3 partakes the preeminent performance evaluation criterion when associated to the other models, where ANFIS-M3 prediction outperforms all other models with NSE, R2, R = 1, and MAPE = 0.261 & 0.006 in both the calibration and verification phases, correspondingly, in the first case, In contrast, the ensemble of BPNN and GPR surpasses all other models in the second scenario, with NSE, R2, R = 1, and MAPE = 0.000, in both calibration and verification phases Comparisons of total performance showed that the proposed models can be a valuable tool for predicting the CS of HSC.

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“…Artificial Neural Network (ANN) is an AI-based approach that not only proved to be effective in handling large amount of dataset, complex nonlinear input and output relationship but also flexible and powerful computational tool [19], [20] . Over the past years, AI in the form of deep learning and machine learning models, have been increasingly applied to solve various environmental engineering problems and has been proved to be very effective, including river water quality modeling [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

Aminu¹

2022

Global J. Eng. Technol. Adv.

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The development of computer models for water quality index forecasting has been a leading research topic worldwide which has been considerably recognized over the last two decades; the balance between efficient water quality requires a good water management technique. The balance is said to be achieved through various procedures many of which require the application of computer-aided forecasting tools. In this paper, a decade research review on the water quality index in the field of artificial intelligence was carried out with the aim to present the most viable or most suitable methods and models to be adopted for future researchers in the field of water quality. The review incorporates the developed models such as ANN, ANFIS, SVM, other regression or time-series, and other soft computing models. This research shows that the study focused on a decade review of the methods and models, and also, there is room for long-term forecasts. It also shows that there is no single AI model that outperforms all the remaining AI models but It is necessary to evaluate the strength of each model combination for each region thus to know what type of method or model that works best for the country or region. The use of AI has grown significantly in recent decades however there is enough room for researchers to duel in and improve in the field of water quality index.

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Application of data-driven algorithms for the forecasting of non-linear parameter

Cited by 5 publications

References 15 publications

Regulation of the fuel injection advance angle in a transport diesel engine

Regulation of the fuel injection advance angle in a transport diesel engine

High-strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm

A novel approach to predict Water Quality Index using machine learning models: A review of the methods employed and future possibilities

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