An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

Behavior evaluation of freundlich and langmuir isotherms in cadmium preconcentration using solid phase extraction method for linear and nonlinear numerical computational patterns

Cadmium is naturally present in the mineral cadmium sulfide which is a rare form of this element and the highest amount of cadmium is obtained from the extraction process of other minerals such as lead, copper and zinc. The release of this metal into the environment leads to widespread epidemiological effects. Therefore, measuring small amounts of this metal is also of particular importance. Small amount measuring methods of this metal are such as,preconcentration using solid phase extraction system using adsorbents. The main part of the preconcentration process is achieved by adsorption processes. In this study, the behavior of Freundlich and Langmuir adsorption isotherms for the capacity of TMON and IMNM adsorbents in cadmium adsorption has been evaluated by Power and Rational statistical distributions. At the end of the study, the constant coefficients of the Freundlich and Langmuir models were compared in both linear and non-linear modes. The results showed; the linearization method for the Kf coefficient of the Freundlich isotherm can cause errors equal to 41.6% in TMON adsorbent and 39.3% in IMNM adsorbent. Also, in parameter b, errors of 66.66% are obtained in TMON adsorbent and 32.45% in IMNM adsorbent

Ranking of cadmium low amount measurement systems according to economic, environmental, and functional indicators using ELECTRE analytical method

Cadmium is one of the transition metals, known by the scientific name Cd. One of its main characteristics is the high toxicity, even in very little amounts. Cadmium is often released through industrial effluents, pesticides, chemical fertilizers, and the burning of fossil fuels. Since the presence of cadmium ions in the living organisms’ body, especially humans, can cause serious damage to the liver and pancreas, and also because its role in causing cancer has been proven, measuring very low amounts of this metal is of high importance. In the first step, this study has reviewed and analyzed common laboratory methods for measuring small amounts of cadmium. Then, according to economic, environmental, feasibility, speed, and accuracy factors, all available methods were evaluated using the ELECTRE technique. The results showed that the extraction methods using Dowex Optipore V-493 resin and extraction system in Triton X-114 surfactant, placed in the first and second positions

Assessment and sensitive analysis of biological water risks in water resources with application of classical mass transfer computations

Due to the urgent need for water in all parts of industrial or developing societies, water supply, and transmission facilities are suitable targets for biological risks. Given that even a short interruption in water supply and water supply operations has a great impact on daily activities in the community, the deliberate contamination of urban water resources has irreparable consequences in the field of public health, and the economy of society will follow. Unfortunately, most officials in the public health control departments in our country have received limited training in detecting accidental or intentional contamination of water resources and dealing with the spread of waterborne diseases both naturally and intentionally. For this reason, there is low preparedness in the responsible agencies to deal with waterborne diseases during biological risks. In the first step of this research, a review study has been conducted on water biological risks and operational strategies to deal with them. In the following, it has studied how Escherichia coli (E. coli) bacteria spread in aqueous media. In this regard, the kinetic model of the studied microorganism was analyzed based on the implementation of (Fick Law) in polar coordinates and the combination of (Dirac Distribution) with (Legendre polynomial) distribution. Finally, after studying the factors affecting the microbial pollutant emission coefficient, the effects of all three factors of linear velocity, linear motion time period, and angle of motion on the pollutant emission flux and biofilm diffusion time in the water supply network environment were investigated. Studies have shown that the linear velocity parameter of Escherichia coli with a nonlinear relationship has the greatest effects on the release of microbial contaminants

Presenting a novel approach for designing chlorine contact reactors by combination of genetic algorithm with nonlinear condition functions, simulated annealing algorithm, pattern search algorithm and experimental efforts

Nowadays, water supplies face critical conditions in terms of quality and quantity. Furthermore, growth in population along with their needs require an increasing level of water-related resources. Consequently, the potential application of purified wastewater supplies can be considered in agriculture, industry, and irrigation of green spaces. Hence the necessity of disinfection and reduction of microbial load in the outlet sewage of water treatment plants are so clear for all designers and operators. Chlorine contact reactors are one of the major pillars of any wastewater treatment plant, whether urban or industrial. A new method is presented in this study based on the optimization of the dispersion amount in a Chlorine Contact Plug Flow Reactor (CCPFR) using single-objective Genetic Algorithm (GA) and nonlinear condition functions, Simulated Annealing Algorithm (SAA) and Pattern Search Algorithm (PSA). Then, it is attempted to assess the hydraulic behavior of the reactor and the microbial load removal performance using statistical, probabilistic and experimental practices. This research was done in a case study of Mashhad city’s wastewater treatment plant. The results of presented study illustrate that GA model has the best outcomes for designing CCPFR and the desired reactor with a depth of 2.45m, width of 1.23m, length of 24.8m, a number of 15 channels, and a retention time of 87 minutes is able to reduce a population of 300000 microorganisms (MPN/100 ml) at the entry to 274 (MPN/100 ml) at the exit. As per this method, investment cost of CCPFR is reduced around 30 percentages in comparison of traditional computation system.</p

Investigation of snow load reduction in the industrial sheds roof design with photovoltaic systems by mathematical modelling, solar system evaluation, X-steel simulation and thermodynamic practices

Since snow load is one of the loads of designing the industrial shed roof, this research presents a new system to reduce the industrial sheds roof design. In this system, sensitive units of moisture and temperature, which can be adjusted with different areas, are installed on the shed&rsquo;s roof. The mechanism of system is that the sensors in the units detect the presence of snow on the shed roof and send an order to connect electricity to the elements; therefore, the snow on the roof melts by the heat generated. In this system, solar panels are used to supply electricity. As with the help of this mechanism, snow does not remain on the roof, it is possible to eliminate the snow load in the calculations of the shed and apply at least the live load of the sixth regulation (Due to having a one-story shed, minimum live load applied and it used only for the foundation design of the structure.), this issue will create an economic plan in shed designing. According to the study conducted in this research, it is shown that the dimensions of the sheet beam used in the shed are reduced, which will significantly reduce the cost of construction and installation to some extent. In the following, two samples of sheds with a span of 20 meters in the presence of snow and the absence of snow in the software were modelled, and the results were compared with each other