Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

Huang, Yongda; Chen, Meiqian

doi:10.1016/j.measurement.2015.06.014

Cited by 28 publications

(10 citation statements)

References 29 publications

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“…The comparison of the results obtained by this model with experimental data shows its effectiveness. Huang et al (2015 and2016) have used two models to predict the thin layer sludge drying. These models are the BackPropagation (BP) and the Generalized Regression Neural Network (GRNN) model.…”

Section: Introductionmentioning

confidence: 99%

Modelisation and Simulation of Heat and Mass Transfers during Solar Drying of Sewage Sludge with Introduction of Real Climatic Conditions

Hassine

Chesneau

Laatar

2017

JAFM

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Sewage sludge presents a real problem with the urban and industrial expanding. So, the drying technique is indispensable in the sludge treatment process to minimize its volume and its revalorization. For cost and environmental reasons, the solar drying is becoming increasingly attractive for small and medium wastewater treatment plants. Therefore, the aim of this work is the modelisation of solar dryer of residual sludge. The model studied is a rectangular agricultural greenhouse. In the lower part, the sludge (assimilated to a porous medium), acts as an absorber. It is subjected to a forced laminar flow. The transfers in the greenhouse and the porous medium are described respectively by the classical equations of forced convection and the DarcyBrinkman-Forchheimer model. The implicit finite difference method is used to discretize the governing differential equation. The algebraic systems obtained are solved using the Gauss, Thomas and Gauss-Seidel algorithms. In order to complete the model and to determine the drying rate we associate a model of the sewage sludge drying kinetics. This work is realized with the meteorological data of the Tataouine region in the south of Tunisia. This data have undergone statistical treatment using the Liu and Jordan method. In order to show the advantages of solar drying, we especially studied the various transfer modes, the drying kinetics and the dryer performance.

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Section: Introductionmentioning

confidence: 99%

Modelisation and Simulation of Heat and Mass Transfers during Solar Drying of Sewage Sludge with Introduction of Real Climatic Conditions

Hassine

Chesneau

Laatar

2017

JAFM

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“…Neural networks are characterized by being universal data approximators. In the context of identification of drying curves, several studies can be found in the literature(Çakmak & Yıldız, 2011;Ge & Chen, 2014;Huang & Chen, 2015;Karimi, Rafiee, Taheri-Garavand, & Karimi, 2012). If an approach similar to the one described in such references had been applied for the present case, a neural network should have been used to approach relative moisture due to drying, air flow and the operation temperature time.…”

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confidence: 99%

Fitting semi‐empirical drying models using a tool based on wavelet neural networks: Modeling a maize drying process

Claumann

Cancelier

Silva

et al. 2017

J Food Process Engineering

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Maize drying is an important process, especially for storage and conservation. For this study, the experimental stage was carried out using a forced convection dryer with air heated at different temperature conditions (306.05–441.85 K) and flow (0.13–0.256 m3/hr), totalizing 15 drying curves. Then the performances of the classic drying kinetics methodology and the approach proposed in this paper, in which the increase in moisture content of the product with time was represented combining exponential models and neural networks based on wavelets, were compared. Good performance was obtained in predictions using the proposed approach. One of the main differentials of the methodology adopted was the obtainment of a model that has a global predictive capacity, within the range of tested operating conditions, which can be used in predicting drying curves for different operating conditions. Practical applications The drying process is also one of the most widely used methods for preserving food, and has the advantage of reducing the costs of storage and transport because of the low volume and weight of the end product. During the last years, this topic has attracted a broad industrial interest, resulting in many research studies investigating the drying process. Usually, with regard to the classic approach for modeling of the drying process, the kinetics of drying curves obtained in different operating conditions is affected separately, that is, the parameters are estimated independently, resulting in different regression problems. With the classical approach, in general, it is not possible to obtain a comprehensive prediction model with regards to operating conditions. We have proposed an alternative modeling method. Aiming to obtain a modeling tool with an overall predictive ability, an approach for drying kinetics prediction that combines exponential models and neural networks was proposed. The proposed modeling method was able to predict drying curves for different operating conditions.

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“…The control system of sludge process can be divided into the thickening control and the dewatering control to reduce the control difficulties. In the thickening control, the different algorithms such as artificial intelligence (AI) techniques [2][3][4][5][6][7][8][9][10][11] have become one Figure 1. The control system of sludge process.…”

Section: Introductionmentioning

confidence: 99%

Research on Controlling Dewatering Ratio Based on Fuzzy PID in Sludge Process

Luo¹,

Liu²,

Liao³

2018

dtcse

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The sludge produced by treating the urban sewage needs thickening and dewatering and it is important to reduce the quantity of discharging sludge by controlling dewatering ratio in the process of sludge thickening and dewatering. In this paper, the fuzzy adaptive PID control algorithm is designed to replace the common PID control to be used to control dewatering ratio in sludge process. The results of the simulation show that the method is effective in improving the control of the dewatering ratio in sludge process 1

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Artificial neural network modeling of thin layer drying behavior of municipal sewage sludge

Cited by 28 publications

References 29 publications

Modelisation and Simulation of Heat and Mass Transfers during Solar Drying of Sewage Sludge with Introduction of Real Climatic Conditions

Modelisation and Simulation of Heat and Mass Transfers during Solar Drying of Sewage Sludge with Introduction of Real Climatic Conditions

Fitting semi‐empirical drying models using a tool based on wavelet neural networks: Modeling a maize drying process

Research on Controlling Dewatering Ratio Based on Fuzzy PID in Sludge Process

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