Saline wastewater treatment by bioelectrochemical process (BEC) based on Al-electrocoagulation and halophilic bacteria: optimization using ANN with new approach

ABSTRACT

In the present study, a bioelectrochemical reactor (BEC) was utilized to treat two types of real saline produced water (PW). BEC was designed based on the combination of electrocoagulation (EC) process with halophilic microorganisms, and it was assessed in terms of biodegradation of hydrocarbons. The effects of various operating parameters including the current density, electrical contact time (On/Off), hydraulic retention time (HRT), and total dissolved solids (TDS) at different levels on the chemical oxygen demand (COD) removal efficiency, settleability, and performance of isolated halophilic microorganisms were examined. Additionally, a novel neural network (ANN) approach modelling using adaptive factors was used to predict and optimize the effects and interactions between operating parameters during BEC process by predicting complicated mechanisms and variations associated with microorganisms. In addition, a new algorithm was developed for the sensitivity analysis to achieve the optimum operating conditions and obtain maximum efficiency in COD removal, sludge volume index (SVI), mixed liquor suspended solids (MLSS), and specific electrical energy consumption (SEEC), simultaneously. BEC was found to be significantly more effective at removing most hydrocarbons, particularly pristine and phytane. In addition, the results showed a significant improvement in settling ability of the biological flocs with average SVI of 91.5 mL/g and a size of 178.25 μm using BEC. Based on estimated operating costs and energy consumption, BEC was more cost-effective and efficient than other bioelectrochemical systems.

GRAPHICAL ABSTRACT

KEYWORDS:

• Produced water
• bioremediation
• artificial neural network
• halophilic microorganisms
• saline wastewater

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Moeen Gholami: Conceptualization, Experiments, Investigation, Formal analysis, Methodology, Data curation, Writing – original draft. Behrooz Abbasi Souraki: Supervision, Project administration, Writing – review & editing. Abbas Shomali: Conceptualization, Formal analysis, Writing, Modelling, Software, Visualization. Alireza Pendashteh: Review, Co-supervision.

Data availability statement

The datasets used and analysed during the current study are available from the corresponding authors upon reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.