A soft computing approach to optimize the clarification process in wastewater treatment

  1. Corral Bobadilla, M. 1
  2. Fernandez Martinez, R. 2
  3. Lostado Lorza, R. 1
  4. Somovilla Gomez, F. 1
  5. Vergara Gonzalez, E.P. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Journal:
Lecture Notes in Computer Science

ISSN: 0302-9743

Year of publication: 2016

Volume: 9648

Pages: 609-620

Type: Article

DOI: 10.1007/978-3-319-32034-2_51 SCOPUS: 2-s2.0-84964067946 WoS: WOS:000389499600051 GOOGLE SCHOLAR

More publications in: Lecture Notes in Computer Science

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Abstract

The coagulation process allows for the removal of colloidal particles suspended in wastewater. Estimating the amount of coagulant required to effectively remove these colloidal particles is usually determined experimentally by the jar test. The configuration of this test is often performed in an iterative manner which has the disadvantage of requiring a significant period of experimentation and an excessive amount of coagulant consumption. This study proposes a methodology to determine the optimum natural coagulant dose while at the same time eliminating the maximum amount of colloidal particles suspended in the wastewater. An estimation of the amount of colloidal particles removed from the wastewater is determined by the turbidity in a standardized jar test, which is applied to the wastewater at the wastewater treatment plant in Logroño (Spain). The methodology proposed is based on the combined use of soft computing techniques and evolutionary techniques based on Genetic Algorithms (GA). Firstly, a group of regression models based on neural networks techniques was performed to predict the final turbidity of a wastewater sample taking into consideration a configuration of jar test inputs. The jar test inputs are: initial turbidity, natural coagulant dosage, temperature, mix speed and mix time. Finally, the best combination of jar test inputs to obtain the optimum natural coagulant dose, while also eliminating the maximum amount of colloidal particles, was achieved by applying evolutionary optimization techniques to the most accurate regression models obtained beforehand. © Springer International Publishing Switzerland 2016.