Experimental determination and modeling of electrical conductivity of natural waters

Master's thesis in the field of chemical engineering, thermokinetic and catalyst orientation

Abstract

The purpose of this research is to estimate the electrical conductivity of natural waters based on modern mathematical methods, and to achieve this, the waters of Karaj and Jajrud rivers are considered as special cases. For this purpose, 20 sampling stations were selected and tests related to the parameters of temperature, alkalinity (pH), turbidity, hardness, total dissolved solids (TDS) and the concentration of main anions and cations were carried out on the samples. Using artificial neural network of MATLAB software, 2137 and 1857 samples for Karaj and Jajrud rivers, respectively, were modeled based on different models, and in these models, 1495 and 1300 samples were used as network training data for Karaj and Jajrud rivers, respectively. Finally, for both Karaj rivers, a special proposed model with sigmoid tangent driving function and Lunberg-Marquard training function was accepted. For the Karaj River, the normal error of the mean square error is 0.033 and the minimum root mean square error is 12.06. Also, the regression value is equal to 0.98 for Jajrud river, the normal error of the mean square error is equal to 0.043 and the minimum root mean square error is 16.97. Also, the value of the correlation coefficient is equal to 0.99.

Key words:

Electrical conductivity, natural waters, Karaj River, Jajrud River, artificial neural network, prediction.

Introduction

The scientific importance of electrical conductivity of electrolyte solutions has attracted the increasing interest of researchers in recent years, and a large amount of laboratory results in This field has been published [1]. In the field of industrial and research activities, there is a need to investigate and pay attention to the important and influential issue of electrical conductivity. It is worth mentioning that the investigation of the electrical conductivity parameter in the field of water purification activities, desalination, agricultural activities (testing soil samples), detection of heat exchanger leakage, detection of water purity and measurement of total dissolved solutes in water, distinguishing between strong and weak electrolytes, obtaining thermodynamic information such as the degree of ionization and many other cases is very important [2].

In engineering applications, knowledge of electrical conductivity for the design and optimization of various processes and devices, especially Those that include electrochemical systems are very important. In the field of corrosion protection of aqueous environments and design of cathodic protection systems, electrical conductivity provides useful information. Also, electrical conductivity can be used to gain insight into the properties of electrolyte solutions and evaluate the values ??of physical characteristics such as dissociation constant [3 and 4].

Electrical conductivity is a scale to determine the ability of a solution to carry an electric current. Conduction in liquids, like conduction in metals, is not carried out by the free movement of electrons, but by ions. All the ions in the solution help to carry the current and ultimately conduct. As a result, by determining the electrical conductivity, the concentration of ionized solutes in an existing sample can be determined [5]. 1-2 Electrical conductivity Electrical conductivity is a physical characteristic that depends on the concentration of ionizable substances in water and shows the ability of water to conduct electricity. The unit of electrical resistance is ohm per meter. Electrical conductivity is the opposite of resistance. In 1971, the Siemens unit, represented by the symbol s, was adopted by the General Conference on Weights and Measures as the SI unit of achievement. As a result, the unit of electrical conductivity of Siemens per meter was determined. The Siemens unit is named after Werner van Siemens [1], a German inventor of the nineteenth century, in the field of electrical engineering [5 and 6]. The electrical conductivity of water is used to detect the sudden changes of minerals dissolved in water, to judge the desirability of the quality of distilled water, to find out the accuracy of some water tests, including in the analysis of parameters such as total dissolved solids [6]. Electrolytes are solutions containing ions resulting from the dissolution of salts or the ionization of substances in the solution. Ions are responsible for the transfer of electric current.Electrolytes are divided into two categories:

Strong electrolytes: these solutions are completely ionized, in fact, the concentration of ions in the solution is proportional to the concentration of the electrolyte. Ionic solids and strong acids are included in this group.

Weak electrolytes: these solutions are not completely ionized. For example, acetic acid is partially separated into acetate ions and hydrogen ions, so the acetic acid solution contains both molecules and ions.

Weak electrolytes have a weaker conductive behavior compared to strong electrolytes, because the number of ions in them is less, as a result, the electric current is carried less between the electrodes [7].

1-2-1 Measuring the electrical conductivity

Electrical conductivity of a solution Electrolyte is formed as a result of the movement of negative ions towards the anode and the movement of positive ions towards the cathode in an electric cell as a result of an applied potential, and it indicates the ability of that solution to pass electricity. In order for the solution to conduct electricity, it must have stable ionic components. Part of the current carried by an ion is related to its relative concentration. The ability of an ion to conduct depends on the charge of the ion and the properties of the solvent (such as viscosity and molecular structure). The increase in heat increases the electrical conductivity of the solution, because due to heat, the viscosity of the solvent decreases [8]. Conductivity is calculated by alternating electric current (I) between two electrodes immersed in the solution and voltage measurement (V). Ohm's law describes this dependence according to the equation (1-1).

(1-1)

Resistance (R) depends on the geometric shape of the conductor (length L and surface A) and electrical conductivity (k).

(1-2)

The ratio of the length to the surface of the conductor is called cell constant[2] and is represented by the symbol K.

According to the equation (1-3) Conductivity [3] (G) is the opposite of resistance [4] (R).

(1-3)

Electrical conductivity [5] (k) is used in aqueous solutions as a parameter to determine the concentration of dissolved ions.

According to equation (1-4) specific resistance [6] (??) is the opposite of electrical conductivity (k).

(1-4)

To determine the electrical conductivity (k), they use the known current and measure the voltage drop, which is directly accessible; the electrical conductivity is determined by taking into account the geometry of the conductor [9 and 10]. unless the mixing of wastewater with industrial effluent changes the electrical conductivity, in which case the electrical conductivity may reach up to 10,000 microsiemens/cm. The electrical conductivity of laboratory distilled water is between 0.5 and 5 microsiemens/cm. Rainwater has an electrical conductivity of 5 to 30 microsiemens/cm, and in surface and drinking water, the electrical conductivity varies between 30 and 2000 microsiemens/cm. The electrical conductivity of ocean water is between 45,000 and 55,000 microsiemens/cm [8].

Abstract:

 

The purpose of this study was to estimate the electrical conductivity of natural water based on the modern mathematical methods considering Karaj and Jajrud Rivers as the special case studies. So the required experimental data were collected from 20 sampling stations. The data included different parameters such as temperature, alkalinity (pH), turbidness, hardness, total dissolved substances (TDS), basic anions and cation density. Different Artificial Neural Networks were designed by MATLAB using 2137 and 1857 total data samples for Karaj and Jajrud case studies, respectively. The training data for these case studies were equal to 1495 and 1300, respectively. The best model for both case studies used sigmoid tangent transfer function as well as the Levenberg-Marquardt training function. The normal mean square error for Karaj River was equal to 0.033, the minimum mean square error was recorded as 12.06 and the correlation coefficient was found to be 0.98.  The normal mean square error for the Jajrud River was equal to 0.