Besides the purification process in the wastewater treatment plant that includes mechanical, biological, and chemical approaches, analysis of hydraulic behavior is also fundamental. This means developing a digital replica of the physical process by simulating the hydraulic parameters. Studying fluid behavior in the plant enables process optimization, improves plant behavior, prevents equipment malfunctions, and more. This paper focuses on defining the concept of a wastewater treatment plant prototype, simulating it, and identifying the available and most suitable software that enables efficient process simulation and validation. The hydraulic parameters, as per the literature review and the proposed concept, that will be simulated are pressure, flow, pressure drop, and water level. MATLAB/Simulink and Python programming languages are considered the most suitable software/programming languages for hydraulic parameters simulation.

In a water quality monitoring project several sensors have electrical contact to each other due to water conductivity. Galvanic isolation is required to avoid electrical interference, which is established in this work by flying capacitor principle. Simultaneously this also provides channel multiplexing for use of a single analog-to-digital converter (ADC). By nature of the circuit, it is the task to measure a decaying voltage caused by capacitor discharge. An error analysis of this situation is given. Furthermore, the usage of an industrial ADC with finite input resistance, built-in lowpass filter, and serial communication with uncertain time behaviour results in additional measurement errors. A method is proposed how to get a measurement under this conditions with acceptable and predictable errors. Finally the analysis is verified with simulation and laboratory measurements.

Water quality index (WQI) has been a universally recognized metric that quantifies water quality, calculated traditionally through analysis of physicochemical characteristics as well as using empirical methods. These conventional approaches can be time-intensive, costly, and may not effectively capture complex relationships among variables. The implementation of machine learning (ML) techniques for more efficient and accurate WQI measurement is the main focus of this paper. Models like Random Forest, Gradient Boosting and so on, required historical data in order to predict WQI dynamically, introducing automation and improving response times. Unique innovations, such as ecological data and hybrid systems, that put together ML and rule-based systems, would improve the accuracy and future development. Results gathered from various experimental data, have demonstrated that ML-based prototypes outperform traditional methods in predictive adaptability, laying foundations for real-time water monitoring solutions.

This paper focuses on the modeling, simulation and optimization of a six-degree-of-freedom (6DOF) robotic arm designed for industrial applications such as assembly, material handling and precision machiing. The study utilizes the Denavit-Hartenberg (DH) parameterization for kinematic modeling. The robot, a Dobot CR5 is modeled in SolidWorks and integrated into MATLAB/Simulink for trajectory optimization using PID controllers. Simulation results demonstrate significant improvements in trajectory accuracy and motion smoothness, validating the proposed optimization approach. This work contributes to advanced control strategies for industrial robotic systems.

Having quality drinking water is a leading factor in maintaining public health, environment protection, and the promotion of sustainable development in industrial and agricultural needs. Measurement and analysis of chemical parameters in drinking water are crucial for ensuring the safety and quality of water for public health. This research was conducted using the spectrophotometry method including a UVVIS DR 6000 HACH Lange spectrophotometer with a wavelength range of 190 to 1100 nm and a highspeed temperature thermostat HT 200 S. Chemical parameters that are measured in this research are chlorides, nitrates, nitrites, potassium, phosphates, and ammonium. All of them are measured on a different wavelength. Samples were taken from three measuring points, all of which are used as drinking water sources and irrigation in the nearby villages. One measuring point is a thermal spring and the other two springs from the foot of a mountain. The water from all three water sources is considerable drinking water. One sample is taken from Vardar River and its water is considered as non-drinking. The results of all measured parameters will be represented through a table and bar graphic chart.

The main focus point of this research is the analysis of the computational methods used for Water Quality Index (WQI) calculation using a real-time measurement System. For the calculations, a prototype of a cheap IoT system for measuring surface water quality has been used. The monitoring system incorporates sensors for temperature, turbidity (NTU), and pH. These parameters are measured through probes submerged directly into the water sample. The probes are connected to an Arduino Uno microcontroller, and through Bluetooth wireless communication, the measured data is transferred to a mobile device and displayed on an application. Also, the measured data are saved on a personal computer for their further processing. The collected data from the IoT system is calculated and presented in real time. Using real-time monitoring we can get instant information for the possible water contamination.

With the rise of sophisticated cyber threats, safeguarding industrial control systems (ICSs) has become increasingly important. This paper delves into physics-based attack detection techniques within Cyber-Physical Systems (CPSs) and highlights their underutilization in Operational Technology (OT) and industrial automation, offering an analysis of current cybersecurity measures and frameworks. By integrating insights from both theoretical and practical perspectives, the study aims towards identifying the key challenges in modernizing legacy systems and stresses the importance of combining Information Technology (IT) with Operational Technology (OT) and emphasizes the importance of bridging the gap between Information Technology (IT) and OT security measures. The paper highlights the integration of physics-based detection methods, the research underscores their critical role in enhancing the security and resilience of ICSs. Conclusions stress the need for increased awareness and adoption of these techniques within OT environments.

This paper represents the artificial neural networks indirect implementation in non-linear control systems. Due to the popularity of the proportional, integral, and derivative, PID controller in control systems, and the process industry, this paper aims to represent the controller constant parameters design. Ziegler-Nichols method is used for setting up the lower and higher limit parameters. Optimizing the PID constant parameters, the ones with the lowest possible mean squared error is done with the artificial neural networks. Python is used as software for proportional, integral, and derivative constant obtaining, and later the results are implemented in the MATLAB/Simulink simulation. The working model is a non-linear hydropower plant with two parallel turbines placed on the same supply pipeline. Simulation models represent the hydropower plant black start. The artificial neural network implementation means providing large input and target data sets for network training and validation. Other important parameters in the network setting are the number of neurons, the number of layers, the type of activation function, the learning rate, the optimizer type, and others.

A prototype of a cheap IoT system for real-time monitoring of river water quality has been developed. The system consists of monitoring stations and appropriate presentation devices (computer, phone, or similar). Each monitoring station has a possibility for real-time measurement of 4 parameters: temperature, pH, turbidity, and dissolved oxygen. They are measured through probes submerged directly in the water. The probes are connected to the Raspberry Pi 3 model B, and through wi-fi communication, the measured data are transferred to the data storage cloud and then transferred to the visualization platform. The collected data in the IoT system is calculated and presented in real-time. Using real-time we can have instant information on the water quality and an alarm for possible contamination. Collected data on the remote server station can be used for deeper water quality analysis.

In this paper, the design process of neural network-based controller application to hydropower plant control system is presented in order to improve the dynamic behavior when only a Proportional-Integral-Derivative controller is in use. Through simulation experiments, the proper function of the neuro-PID control technique has been successfully verified. Ensuring better results rather than using only gain scheduling PID control lies in designing a suitable neuro controller. That includes sizing the right number of neurons in each layer, a number of hidden and output layers, fitting and training the network behind the neuro-controller, and data normalization. Activation function type determination in each layer is also an important parameter that depends on the output response of the system. In this paper, MATLAB® - Deep Learning Toolbox is used, whereas the simulations are prepared in Simulink. The obtained results show that the optimized turbine model gives a slight improvement in the behavior of the hydropower plant.

Cyber-Physical Systems (CPSs) are becoming more relevant, and ever more present in today's world. The integration of cyber/computer systems with the physical world gives improvements on various efficiency and reliability parameters, as well as convenience. Unfortunately, by their very nature CPSs come with various risks and complications that come from the integration of the cyber and physical worlds. One of these risks are cyber and physical attacks on CPSs. Research across various disciplines on these attacks is growing each passing day. In this paper we simulate the possible effects of False Data Injection Attacks (FDIAs) on a hybrid nonlinear model of a hydropower plant (HPP), and then we implement a classical control engineering method - Kalman filtering, as a detection system for these attacks. It is proven that a Kalman filter designed on a linearized continuous system model is a successful detection method in various attack scenarios on the nonlinear hybrid system model.

The Paris Agreement signed in 2015, unites nearly 200 countries on the goal of limiting global warming and lowering carbon emissions. The goal of lowering carbon emissions can mainly be achieved by lowering fossil fuels within the energy sector since their use in electricity generation is a significant contributor to greenhouse gas emissions. This is why European countries, as well as North Macedonia, are planning to reduce the amount of climate-damaging fossil-fueled power plants, but to cover electricity demand, the share of renewable energies (in North Macedonia mostly wind and solar energy, here addressed as intermittent renewable energy sources - IRES) within their power systems must be increased. Changing power systems and including higher shares of IRES will increase imbalances and thus the need for flexibility (refers to means able to balance electricity supply and demand). Imbalances have always occurred in power systems, resulting from various reasons, and power systems are able to react with flexibility. Considered options, categorized into four kinds of flexibility (dispatchable generation, demand side management, increased interconnection, and energy storage), all offer possibilities to balance the systems by different approaches. Although all providing individual advantages, hydropower, especially pumped storage hydropower, was identified in this paper to be a very flexible, diverse option and a technology capable of meeting flexibility challenges set by the increase of IRES. The need for flexibility in the hydropower production could be improved using variable-speed turbines instead of multiple start-stops depending on the grid demand.

A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. In this paper, the focus is put on the mechanical power responses improvement and the advantages of implementing new technologies contrary to the problems that can occur by using them such as plant destabilization by implementing minor changes, fitting parameters, learning, and training processes, number of hidden layers/neurons, number of epochs, etc.

A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from the working group of IEEE on prime movers, is represented in this paper. HPP stability and high efficiency are important factors dependent on the dynamic changes in the energy system demands and the starting time of the plant because the obtained energy is very flexible to those changes in the energy system. This paper is shown and analysed the implementation of the artificial neural network-based controller with PID as an auxiliary controller which helped achieve better behaviour, faster plant stabilization, and operation. The benefits of new technologies and possibilities led to improvements in HPP control and faster system operation. This is achieved by using MATLAB® – Deep Learning Toolbox whereas the simulations are prepared in Simulink. Artificial Neural Networks (ANN) as a technique used in the HPP control systems have advantages in getting a stable and faster response but the complexity of the structure behind the neural networks (NN), meaning algorithms, number of hidden layers, training function, activation function can complicate and destabilize the process. In this paper, the focus is put on the mechanical power responses improvement and the advantages of implementing new technologies contrary to the problems that can occur by using them such as plant destabilization by implementing minor changes, fitting parameters, learning, and training processes, number of hidden layers/neurons, number of epochs, etc.

Modern oil-hydraulic systems for moving heavy payloads are designed for optimised motion, but also for minimal energy loss. Individual metering technique, using separate control of the two actuator chambers, offers some advantages. A common strategy when moving the load is to control the incoming oil flow to obtain a desired speed, and the pressure at the downstream side for good efficiency. In this work analysis and design of PI (proportional-integral) pressure control is done. The adjustment of the control parameters of this loop is usually uncritical. In the worst case, the damping of the mechanical system is the only contribution. It is shown in this work, that pressure control can increase the damping of load oscillations. The influence of the P and I parameters to the system properties is investigated using the poles of the transfer function of the system. It is shown, that there is a point, where the damping factor of the system has its maximum value, and a design method for this optimisation is given. The problem ends up in a system of two equations of fourth order. A method is shown how to reduce the problem to solving one third-order equation, which is done numerically. Finally, the results are verified using simulation.

Design of a Hardware-in-the-loop test bench for simulation and control of greenhouse climate is presented. This test bench is used for setting and tuning the climate controller system used for achieving preferred conditions in the greenhouse. This is a complicated task since it is very well known that an agricultural greenhouse is a complex dynamic system consisting of dozens of elements that can interact and exchange energy between them and the environment. Thus, firstly, mathematical modeling of the greenhouse is accomplished using differential equation to describe the dynamic aspects of the greenhouse climate and the interaction between the elements. Secondly, the potential of the designed control system is demonstrated by both simulation and experimental tests. The simulation is performed with the software Simulink® MATLAB®. Additionally, for the purpose of collecting data from a real greenhouse, a measurement system was constructed, consisted of an industrial Raspberry Pi and industrial sensors for remote sensing of the agricultural parameters. It uses a data logging system for viewing, analyzing and storing the data of the greenhouse.

Friction is a repeatable and undesirable problem in hydraulic systems where always has to be a tendency for its removal. In this paper, the friction model is presented through which the most accurate results are achieved and the way of friction compensation, approached trough technique presented with the mathematical model of a hydraulic cylinder of a hydro turbine wicket gate controlled by a servomechanism. Mathematical modelling of a servo mechanism and hydraulic actuator, and also the simulation of hydraulic cylinder as a part of a hydro turbine wicket gate hydraulic system where the stick-slip phenomenon is present between the system components that are in contact is presented. Applied results in this paper and the theory behind them precisely demonstrate under what circumstances the stick-slip phenomenon appears in such a system. The stick-slip effect is simulated using Simulink and Hopsan software and the analysis of the results are given in this paper. Removal of the stick-slip effect is presented with the design of a cascade control implemented to control the behaviour of the system and remove the appearance of a jerking motion.

Air pollution is an extraordinarily big problem in developing countries, as well as some metropolitan cities. Infiltration of the outdoor air inside buildings, together with other factors can significantly lower the indoor air quality. Particulate matter (PM) pollution is recognized as one of the biggest problems with regards to indoor air quality. An increasingly popular method for dealing with indoor PM pollution is the use of mobile air purifiers. In this paper, a mathematical model for the indoor PM pollution, and the effect of the purifier is presented. This model is verified using an experimental setup. Then an automatic control system is developed based on the model. Finally, a Hardware-in-the-loop (HIL) simulation of the control system is performed using a Raspberry Pi 4 B and an ST Nucleo-64 board.

Mobile Robot navigation in real environment, detecting and avoiding static and dynamic obstacles is still a challenging task even counting new technology development. This consists, on methods and algorithms applied in order to achieve most optimal results. We present application of Fuzzy Logic Controller (FLC) and PID controller, which will deal with, signal processing of three sensors as inputs of FLC and two outputs which are left and right motors as actuators. Outputs from FLC will be sent to PID controller in order to get better and more accurate results for mobile robot navigation respectively more exact robot movement. In this paper is presented, a navigation of the mobile robot via FLC and PID controller.