scholarly journals NEURO-FUZZY MODELLING IN ANAEROBIC WASTEWATER TREATMENT FOR PREDICTION AND CONTROL

2014 ◽  
pp. 51-56
Author(s):  
Snejana Yordanova ◽  
Rusanka Petrova ◽  
Nelly Noykova ◽  
Plamen Tzvetkov
Keyword(s):  
Wastewater Treatment ◽  
Prediction Models ◽  
Biogas Production ◽  
Organic Load ◽  
Model Parameters ◽  
Box Models ◽  
Neuro Fuzzy ◽  
Prediction And Control ◽  
And Control

The aim of the present paper is to develop neuro-fuzzy prediction models in MATLAB environment of the anaerobic organic digestion process in wastewater treatment from laboratory and simulated experiments accounting for the variable organic load, ambient influence and microorganisms state. The main contributions are determination of significant model parameters via graphical sensitivity analysis, simulation experimentation, design and study of two “black-box” models for the biogas production rate, based on classical feedforward backpropagation and Sugeno fuzzy logic neural networks respectively. The models application is demonstrated in process predictive control

Innovative Computing Techniques for Development of an Integrated Computer Control System

1992 ◽  
Vol 26 (5-6) ◽  
pp. 1365-1374 ◽  
Author(s):  
G. G. Patry ◽  
M. W. Barnett
Keyword(s):  
Wastewater Treatment ◽  
Object Oriented Programming ◽  
Plant Performance ◽  
Organic Load ◽  
Model Parameters ◽  
Process Data ◽  
Wastewater Treatment Process ◽  
Deterministic Modelling ◽  
Linear Differential ◽  
And Control

Over the past decade there has been a shift in emphasis from design and construction of wastewater treatment facilities to operation. Poor plant performance, high costs and damage to the environment have resulted from operational problems. Wastewater treatment consists of a complex sequence of inter-dependent biological, physical and chemical processes subject to time-varying hydraulic and organic load conditions. Wastewater treatment process operation and control is a knowledge intensive task. Research on improving operation and control has centred on identifying important mechanisms responsible for observed behaviour and modelling both the process and optimum ways of operating the process. These models have served as useful tools for improving operation and control. Many different approaches have been used, including deterministic modelling, stochastic modelling and, more recently, linguistic modelling. Complex mathematical models of wastewater treatment processes consisting of large numbers of non-linear differential equations can be constructed using tools such as the General Purpose Simulator (GPS) and, given appropriate data, model parameters can be evaluated and updated using existing optimization routines. Object oriented programming (OOP) and a model based reasoning (MBR) approach provides a useful framework for development of deep-knowledge expert systems (ES). Data-driven modelling methods, including both time series analysis and artificial neural network (ANN) techniques, can also be employed to make maximum use of information contained in process data. Each of these model types is a necessary component of a computer system for operational control of wastewater treatment but, in isolation, none are sufficient for making the system robust. An integrated environment for combining these techniques has been developed for this purpose and the basis for its development is described.

scholarly journals Furnace heat prediction and control model and its application to large blast furnace

2019 ◽  
Vol 38 (2019) ◽  
pp. 884-891
Author(s):  
Zhuang-nian Li ◽  
Man-sheng Chu ◽  
Zheng-gen Liu ◽  
Gen-ji Ruan ◽  
Bao-feng Li
Keyword(s):  
Blast Furnace ◽  
Control Model ◽  
Heat Index ◽  
Model Parameters ◽  
Stable Operation ◽  
Oxygen Balance ◽  
Large Blast ◽  
Large Blast Furnace ◽  
Prediction And Control ◽  
And Control

AbstractBlast furnace heat is the key to the blast furnace’s high efficiency and stable operation, and it is difficult to maintain a suitable temperature for large blast furnace operations. When designing the furnace heat prediction and control model, parameters with good reliability and measurability should be chosen to avoid using less accurate parameters and to ensure the accuracy and practicability of the model. This paper presents an effective model for large blast furnace temperature prediction and control. Using thermal equilibrium and the carbon-oxygen balance of the blast furnace’s high-temperature zone, the slag-iron heat index was calculated. Using the relation between the molten iron temperature and slag-iron heat index, the furnace heat parameter can be calculated while production conditions are changed,which can guide furnace heat control.

scholarly journals Application of ultrasonic disintegration to waste activated sludge for increasing of biogas production by anaerobic digestion

2020 ◽  
Vol 2 (2) ◽  
pp. 17-21
Author(s):  
Ion Viorel Patroescu ◽  
Razvan Laurentiu Dinu ◽  
Mihai Stefanescu ◽  
Valeriu Robert Badescu ◽  
Nicolae Ionut Cristea ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Biogas Production ◽  
Treatment Plant ◽  
Experimental Tests ◽  
Organic Load ◽  
Municipal Wastewater Treatment ◽  
Biological Sludge ◽  
Sludge Stabilization

The municipal wastewater treatment is the source of significant amounts of primary and secondary sludge which is under the present legislation referring to quality and management aspects. It is estimated that a half of wastewater treatment plant costs are due to the sludge management. Anaerobically sludge stabilization, capitalization as energy source, in order to diminish the costs and sludge volume decreasing, are the aims of the main operational steps of sludge treatment, as a part of wastewater treatment plant. The improvement of sludge anaerobically stabilization process must be possible by acting in the rate limiting step - hydrolysis in order to rise the organic carbon solubilization. The increase of soluble carbon can be possible by adding a pretreatment step of waste biological sludge, ultrasonic disintegration being one option. This paper emphasized the experimental results regarding anaerobically stabilization of the thickened waste biological sludge by ultrasonication taking into account the results of blank test, without ultrasonication. Experimental tests show that ultrasonic disintegration of the sludge having initial dried substances content (d.w) 2.72% and soluble organic load COD of 598 mg O2/L led to soluble COD concentration of 4950-6710 mg O2/L after sonication with specific energy in the range of 3.06 - 14.24 kWh/kg d.w. Anaerobically stabilization during 25 test days at 36 0C of the mixture 40% disintegrated biological sludge and 60% digested sludge (inoculum) mixture led to 30-38.6% increase of biogas production comparing with parallel test with non-sonicated sludge.

scholarly journals Effect of sewage sampling frequency on determination of design parameters for municipal wastewater treatment plants

2020 ◽  
Author(s):  
T. Gehring ◽  
E. Deineko ◽  
I. Hobus ◽  
G. Kolisch ◽  
M. Lübken ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Oxygen Demand ◽  
Sampling Frequency ◽  
Organic Load ◽  
Design Parameters ◽  
Data Sets ◽  
Municipal Wastewater Treatment

Abstract The uncertainty associated with the determination of load parameters, which is a key step in the design of wastewater treatment plants (WWTPs), was investigated on basis on data sets from 58 WWTPs. A further analysed aspect was the organic load variations associated with variable sewage temperatures. Data from 26 WWTPs with a high inflow sampling frequency was used to simulate scenarios to investigate the effect of lower sampling frequencies through a Monte Carlo approach. The calculation of 85-percentile values for chemical oxygen demand (COD) loadings based on only 26 samples per year is associated with a variability of up to ±18%. Approximately 90 samples per year will be necessary to reduce this uncertainty for estimation of COD loadings below 10%. Hence, a low sampling frequency can potentially lead to under- or overestimation of design parameters. Through an analogous approach, it was possible to identify uncertainties of ±11% in COD loading when weekly average data was used with 4 samples per week. Finally, a tendency of lower COD input loads with increasing temperatures was identified, with a reduction of about 1% of the average loading per degree Celsius.

scholarly journals Performance of up flow anaerobic sludge fixed film bioreactor for the treatment of high organic load and biogas production of cheese whey wastewater

2015 ◽  
Vol 21 (2) ◽  
pp. 229-237 ◽  
Author(s):  
Nazila Tehrani ◽  
Ghasem Najafpour ◽  
Mostafa Rahimnejad ◽  
Hossein Attar
Keyword(s):  
Wastewater Treatment ◽  
Biogas Production ◽  
Oxygen Demand ◽  
Pilot Scale ◽  
Cod Removal ◽  
Organic Load ◽  
Anaerobic Sludge ◽  
Operation Temperature ◽  
Fixed Film ◽  
Fixed Film Bioreactor

Among various wastewater treatment technologies, biological wastewater treatment appears to be the most promising method. A pilot scale of hybrid anaerobic bioreactor was fabricated and used for the whey wastewater treatment. The top and bottom of the hybrid bioreactor known as up flow anaerobic sludge fixed film (UASFF); was a combination of up flow anaerobic sludge blanket (UASB) and up flow anaerobic fixed film reactor (UAFF), respectively. The effects of operating parameters such as temperature and hydraulic retention time (HRT) on chemical oxygen demand (COD) removal and biogas production in the hybrid bioreactor were investigated. Treatability of the samples at various HRTs of 12, 24, 36 and 48 hours was evaluated in the fabricated bioreactor. The desired conditions for COD removal such as HRT of 48 hours and operation temperature of 40 ?C were obtained. The maximum COD removal and biogas production were 80% and 2.40 (L/d), respectively. Kinetic models of Riccati, Monod and Verhalst were also evaluated for the living microorganisms in the treatment process. Among the above models, Riccati model was the best growth model fitted with the experimental data with R2 of about 0.99.

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