Using Extreme Learning Machine for Filamentous Bulking Prediction in Wastewater Treatment Plants

2015 ◽  
pp. 1-9
Author(s):  
Yuchao Zhao ◽  
Zhengchao Xie ◽  
Inchio Lou
Keyword(s):  
Wastewater Treatment ◽  
Extreme Learning Machine ◽  
Wastewater Treatment Plants ◽  
Filamentous Bulking ◽  
Learning Machine

Controlling the Settling of Activated Sludge in Pulp and Paper Wastewater Treatment Plants

1999 ◽  
Vol 40 (11-12) ◽  
pp. 223-229 ◽  
Author(s):  
Frédéric Clauss ◽  
Christel Balavoine ◽  
Delphine Hélaine ◽  
Gaëtan Martin
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plants ◽  
Pulp And Paper ◽  
Organic Load ◽  
Forest Industry ◽  
Filamentous Bulking ◽  
Mineral Particles ◽  
Mineral Powder

Forest industry wastewaters are difficult to clean: hydraulic and organic load variations, filamentous bulking or pin-point flocs negatively impact depollution processes. The addition of a fine, mineral, talc-based powder, Aquatal, into the aeration tanks of wastewater treatment plants connected to pulp and paper factories has been successfully tested since end of '97. The first case-study presents full results obtained over a period of 18 months in a 20,000 p.e. plant connected to a paper factory. The mineral powder was regularly added to control sludge volume index, thereby ensuring low suspended solids concentration in the outfluent. Plant operators could easily adapt biomass concentration to match organic load variation, thereby maintaining pollution micro-organisms ratio constant. In a second case study, a trouble-shooting strategy was implemented to counteract filamentous bulking. A one-off, large dosage enabled the plant operator to deal effectively with poor settleability sludge and rapidly control sludge blanket expansion. In both cases, the main common characteristics observed were an increase in floc aggregation and the production of heavier and well-structured flocs. The sludge settling velocity increased and an efficient solid/liquid separation was obtained. After a few days, the mineral particles of Aquatal were progressively integrated into the sludge floc structure. When the mineral powder was added to the activated sludge in the aeration basin, chemical interactions frequently encountered with other wastewater treatment additives did not pose a problem. Moreover, with this mineral additive, the biological excess sludge displayed good thickening properties and dewatering was improved. Despite the addition of the insoluble mineral particles, the amount of wet sludge expelled did not increase. Aquatal offers a rapid solution to floc settleability problems which so frequently arise when physical or biological disorders appear in forest industry wastewater treatment plants.

scholarly journals A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Measurement Data ◽  
Estimation Method ◽  
Cuckoo Search ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

Including the effects of filamentous bulking sludge during the simulation of wastewater treatment plants using a risk assessment model

2009 ◽  
Vol 43 (18) ◽  
pp. 4527-4538 ◽  
Author(s):  
Xavier Flores-Alsina ◽  
Joaquim Comas ◽  
Ignasi Rodriguez-Roda ◽  
Krist V. Gernaey ◽  
Christian Rosen
Keyword(s):  
Risk Assessment ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Filamentous Bulking
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