A data-driven model for maximization of methane production in a wastewater treatment plant

2012 ◽  
Vol 65 (6) ◽  
pp. 1116-1122 ◽  
Author(s):  
Andrew Kusiak ◽  
Xiupeng Wei
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Methane Production ◽  
Ph Value ◽  
Treatment Plant ◽  
Daily Basis ◽  
Data Driven ◽  
Volatile Solids ◽  
Data Driven Approach ◽  
Model Temperature

A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction. First, a prediction model of methane production was built by a multi-layer perceptron neural network. Then a particle swarm optimization algorithm was used to maximize methane production based on the model developed in this research. The model resulted in a 5.5% increase in methane production.

Co-digestion of glycerine and sewage sludge to optimise green electricity production

2013 ◽  
Vol 67 (12) ◽  
pp. 2863-2867 ◽  
Author(s):  
L. Maes ◽  
M. Weemaes ◽  
N. Hellinck ◽  
G. De Gueldre ◽  
B. Van De Steene
Keyword(s):  
Wastewater Treatment ◽  
Sewage Sludge ◽  
Wastewater Treatment Plant ◽  
Methane Production ◽  
Treatment Plant ◽  
Crude Glycerine ◽  
Electricity Production ◽  
Green Electricity ◽  
Biogas Yield ◽  
The Difference

Sewage sludge and crude glycerine were co-digested in the mesophilic digester of Hoogstraten wastewater treatment plant. Additions of up to 1 kg of crude glycerine/(m³ reactor).(day) were done without significant operational problems. At higher dosage, severe digester foaming was observed. Methane production during co-digestion was up to 20% higher than what would be expected based on the digester input. Compared to the period before glycerine dosage, every tonne of added crude glycerine resulted in a surplus methane production of 489 Nm³. The theoretical methane production from the used crude glycerine was 341 Nm³ per tonne. The difference is explained by a higher sewage sludge degradability during co-digestion with glycerine. Glycerine dosage can remedy the lowered specific biogas yield of sewage sludge in Flanders and consequently enhance green electricity production.

Automated manometric method to assess anaerobic toxicity of chemicals

2006 ◽  
Vol 54 (2) ◽  
pp. 95-101 ◽  
Author(s):  
F. Fdz-Polanco ◽  
P. Nieto ◽  
S.I. Pérez-Elvira ◽  
M. Fdz-Polanco
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Methane Production ◽  
Treatment Plant ◽  
Inhibitory Effect ◽  
Commercial Products ◽  
Methanogenic Activity ◽  
Substrate Removal ◽  
Manometric Method ◽  
Methane Production Rate

Industrial additives eventually used for different purposes (antifoaming, cleaning, bactericides, antiscale, etc) are discharged to the wastewater treatment plant. The anaerobic toxicity of these commercial products is not provided by suppliers. A new manometric method is developed and tested to evaluate anaerobic toxicity or inhibition using four different commercial products. Antifoaming Cleron 6 (50–200 ppm), bactericide Divosan-forte (0.05–1.0% v/v), bleach (0.1–1.0% v/v) and cleaning agent Topax 66 (0.10–1.0% v/v). According to the different methods proposed in the literature, from the methane production rate, it is possible to calculate both methanogenic activity evolution and final substrate removal and quantify the potential inhibitory effect of commercial additives. The experimental method is simple and reliable.

An integrated approach to analyse biofilms of a full scale wastewater treatment plant

2002 ◽  
Vol 46 (1-2) ◽  
pp. 199-206 ◽  
Author(s):  
M. Martín-Cereceda ◽  
B. Pérez-Uz ◽  
S. Serrano ◽  
A. Guinea
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Integrated Approach ◽  
Full Scale ◽  
Ciliate Species ◽  
Volatile Solids ◽  
Composition And Structure ◽  
Three Stages ◽  
Biofilm Structure

A rotating biological contactor (RBC) system operating in a full-scale wastewater treatment plant has been described by several approaches accounting for performance, composition and structure of biofilms in three stages through biological wastewater treatment (RBC1, RBC 2, RBC 3). RBC biofilms were effective in removing the BOD loading from 13 g BOD5 d−1 m−2 in RBC 1 to 6 g BOD5 d−1 m−2 in RBC 3. Analysis of biofilm composition showed: i) the volatile solids were similar in the three RBCs (0.6 g m−2 VS per g m−2 of TS); ii) the protozoan and metazoan biocenosis was mainly made up of ciliated protozoa, which were most abundant in RBC 2 (1.84 × 106 ciliates g−1 VS). Relationship between ciliate species and physical-chemical profile of the system by cluster analysis indicated that the species Acineria uncinata, Amphileptus punctatus, Cinetochilum margaritaceum and Holosticha mancoidea were associated with the best RBC performance; iii) the exopolymeric matrix of the three RBC biofilms was mainly constituted by proteins, although humic substances, polysaccharides, uronic acids and DNA were also found. Analysis of biofilm structure by confocal microscopy indicated changes in biofilm organisation with depth. Results have been brought together and a graphic representation of the composition and architecture of RBC biofilms is presented.

Adaptive optimal control for a wastewater treatment plant based on a data-driven method

2013 ◽  
Vol 67 (10) ◽  
pp. 2314-2320 ◽  
Author(s):  
Jun-Fei Qiao ◽  
Ying-Chun Bo ◽  
Wei Chai ◽  
Hong-Gui Han
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Wastewater Treatment Plant ◽  
Mechanistic Model ◽  
Full Range ◽  
Treatment Plant ◽  
Operating Conditions ◽  
Data Driven ◽  
Evaluation Module ◽  
Operating Points

In order to optimize the operating points of the dissolved oxygen concentration and the nitrate level in a wastewater treatment plant (WWTP) benchmark, a data-driven adaptive optimal controller (DDAOC) based on adaptive dynamical programming is proposed. This DDAOC consists of an evaluation module and an optimization module. When a certain group of operating points is given, first the evaluation module estimates the energy consumption and the effluent quality in the future under this policy, and then the optimization module adjusts the operating points according to the evaluation result generated by the evaluation module. The optimal operating points will be found gradually as this process continues repeatedly. During the optimization, only the input–output data measured from the plant are needed, while a mechanistic model is unnecessary. The DDAOC is tested and evaluated on BSM1 (Benchmark Simulation Model No.1), and its performance is compared to the performance of a proportional-integral-derivative (PID) controller with fixed operating points under the full range of operating conditions. The results show that DDAOC can reduce the energy consumption significantly.

scholarly journals Time and Place Changes in pH And Conductivity in the Water of Čaradice Stream / Časov É a Priestorov É Zmeny pH a Vodivosti vo Vode Čaradického Potoka

2013 ◽  
Vol 16 (1) ◽  
pp. 24-28 ◽  
Author(s):  
Jaroslav Noskovič ◽  
Mária Babošová ◽  
Jana Porhajašová ◽  
Andrej Tárník ◽  
Viktor Varga
Keyword(s):  
Wastewater Treatment ◽  
Water Flow ◽  
Wastewater Treatment Plant ◽  
Ph Value ◽  
Treatment Plant ◽  
Water Reservoir ◽  
Sampling Time ◽  
Average Value ◽  
The Village ◽  
Research Period

Abstract During 2005-2010, we measured pH and conductivity values in the watercourse Čaradice stream, which flows from its spring in Pohronský Inovec Mountains. The average pH value for the whole research period was 7.76. Depending on the time of sampling, its maximum average value for the whole period was found in July and August (7.96) and minimum average value was detected in the month of January (7.64). Depending on the sampling sites, its lowest average value for the whole period was recorded under the forest eco-system of Pohronský Inovec (7.39) and the highest under the water reservoir (8.0), which could have been caused by a greater number of autotrophic organisms in the water of the water reservoir, which reduced soluble CO2 from the water. The average conductivity value for the whole research period was 39.72 mS.m-1. The lowest average value, depending on the sampling time throughout the research period, was found in March (23.49 mS.m-1) which was probably caused by snowmelt and higher water flow in the watercourse, and maximum in the month of September (54,88 mS.m-1). Depending on the sampling sites, the highest average value (51.62 mS.m-1) was found near the village Kozárovce, which is possibly caused by releasing unclean wastewater into the watercourse, as the wastewater treatment plant has not been built in the village.

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