Empiricalcapacitive deionization ANN nonparametric modelingfor desalination purpose

Abstract EN

This paper proposes Capacitive Deionization (CDI) Operational Conditions Nonparametric Modeling for desalination purposes.

CDI technique is advantageous due to its low energy consumption, low environmental pollution, and low fouling potential.

The objective of this paper is to model the investigation of different operational conditions (Total Dissolved Solids (TDS) concentration, temperature, flow rate) effect on the CDI electrosorption efficiency and energy consumption.

The modeling based on real experimental data with Laboratory scale experiments were conducted by using a commercial CDI with activated carbon electrodes developed by Aque EWP [1], as a training data and express them as algebraic functions to connect between various operational characteristics.

This is done by developing four models with the aid of Artificial Neural Network (ANN).

First one to express electrosorptive performance of CDI at different solution temperatures with Temperature and Time as inputs and TDS as output.

Second one for Efficiency as output with Temperature, Time and TDS as inputs.

Third one to illustrate effect of flow rate on electrosorption efficiency and energy consumption with Flow Rate and Time as inputs and TDS as output.

Forth one for Energy Consumption as output and Operational Flow Rate, Time and TdS as inputs.

All characteristics are well depicted in the form of 3D figures as the training data for ANN models to show the validity of the proposed technique in interpolations and estimations.

ANN technique models are adopted for various characteristics estimation process and generation of functions for theses experimental data due to its advantages.

ANN models are created with suitable numbers of layers and neurons, which trained, simulated, checked, verified and their algebraic equations are concluded accurately with excellent regression constants.