THE CHOOSING OF THE MOST ENERGY-EFFICIENT AND FINANCIALLY JUSTIFIED METHOD OF REGULATION OF THE BOILER HOUSES WITH THE USE OF A CONTROL SYSTEM

ABSTRACT

Boiler control systems play a pivotal role in managing heating systems, ensuring temperature regulation, and optimizing energy consumption. This article conducts an extensive market analysis, comparing various methods, brands, and prices of boiler control systems offered by European, Russian, Kazakhstani, and Asian companies.

Based on market analysis and energy efficiency calculations, Fuzzy Logic Control emerges as the most technically and financially justified regulation method. Its adaptability to changing conditions, handling of uncertainties, and potential for energy savings make it the preferred choice for optimizing boiler control systems. While its initial cost may be higher, the long-term benefits in terms of energy savings and customer satisfaction make it a wise investment.

Keywords: boiler houses, control system.

INTRODUCTION

Boilers are ubiquitous in many industrial and commercial processes, from power generation to manufacturing and chemical processing. They play a critical role in providing the necessary heat, steam, or hot water for these operations. However, operating a boiler can consume a significant amount of energy, which can lead to high operating costs and environmental impacts. Therefore, it is essential to implement a control system that regulates the boiler's temperature schedule in an energy-efficient and financially justified manner.

The primary purpose of a control system is to ensure that the boiler operates at the desired temperature to meet the process requirements while minimizing energy consumption and costs. In essence, a control system adjusts the boiler's operational parameters to maintain the set temperature within a narrow range. A well-designed control system can provide several benefits, such as improved process efficiency, reduced energy costs, enhanced safety, and minimized environmental impacts.

There are various methods of regulating a boiler's temperature schedule with the use of a control system, each with its advantages and disadvantages. One of the most common methods is on/off control, which involves turning the boiler on or off based on a set temperature threshold. While this method is relatively simple and inexpensive, it can lead to large temperature swings and energy waste, especially when the process load varies.

Another method is proportional control, which adjusts the boiler's firing rate proportional to the temperature deviation from the set point. Proportional control can provide better temperature control than on/off control, but it still may not be sufficient for applications with highly variable process loads.

A more advanced method is proportional-integral-derivative (PID) control, which combines proportional, integral, and derivative control actions to provide a more precise and stable temperature control. PID control calculates the deviation between the set point and the actual temperature, then adjusts the firing rate based on proportional, integral, and derivative control gains. PID control can provide excellent temperature control under varying process loads, but it requires more sophisticated control algorithms and hardware, which can increase the system's complexity and cost.

The choice of the most energy-efficient and financially justified method of regulating a boiler's temperature schedule with the use of a control system depends on several factors, such as the process requirements, the boiler's size and capacity, the operating conditions, and the energy cost structure. Therefore, a thorough analysis of these factors is essential to determine the optimal control strategy for a given application.

Implementing an energy-efficient and financially justified method of regulating a boiler's temperature schedule with the use of a control system can provide significant benefits in terms of improved process efficiency, reduced energy costs, enhanced safety, and minimized environmental impacts. By selecting the most appropriate control strategy and optimizing the boiler's operational parameters, businesses can achieve these benefits while meeting their process requirements and financial goals.

Aim of the research is determination of the optimal method of regulating the boiler system to maintain a required level of energy efficiency.

Tasks of research:

-analysis of the existing methods of the boiler system regulation (market research);

-determination of the most technically effective method of boiler system regulation;

-determination of the most economic profitable method of boiler system regulating.

The object of research is control systems.

The subject of research is control systems methods of regulation.

Relevance of the research: The energy efficiency and financial viability of industrial processes have become a significant concern for businesses and policymakers worldwide. Energy consumption in industrial processes accounts for a considerable share of the total energy consumption and optimizing the energy use can lead to substantial reductions in operating costs and environmental impacts.

One of the primary sources of energy consumption in industrial processes is the boiler, which provides heat, steam, or hot water for various operations.

The choosing of the most energy-efficient and financially justified method of regulating the boiler's temperature schedule with the use of a control system is a relevant topic in this context.

Regulating the boiler's temperature schedule is essential to meet the process requirements while minimizing energy consumption and costs. However, the choice of the most appropriate control strategy depends on several factors, such as the process requirements, the boiler's size and capacity, and the energy cost structure. Therefore, a thorough analysis of these factors is essential to determine the optimal control strategy for a given application.

While there have been several studies on the optimization of boiler operation, this paper's novelty lies in the detailed analysis of the different methods of regulating a boiler's temperature schedule with the use of a control system.

 The paper provides a comprehensive comparison of the energy efficiency and financial viability of various control strategies, such as on/off control, proportional control, and PID control. Furthermore, the paper highlights the importance of selecting the most appropriate control strategy for a given application to achieve the desired temperature while minimizing energy consumption and costs.

The relevance of this paper is significant in the context of the global efforts to reduce greenhouse gas emissions and mitigate climate change. Boilers are one of the significant sources of energy consumption and emissions in many industrial processes, and optimizing their operation can lead to substantial reductions in energy use and environmental impacts.

Therefore, the paper's findings can help businesses and policymakers make informed decisions on energy management and climate action.

Moreover, the paper's relevance extends beyond the industrial sector, as boilers are also used in residential and commercial buildings for heating and hot water. By optimizing the boiler's operation, homeowners and building owners can reduce their energy bills and carbon footprint, contributing to the global efforts to combat climate change.

In conclusion, the choosing of the most energy-efficient and financially justified method of regulating the boiler's temperature schedule with the use of a control system is a relevant and novel topic that can contribute to the sustainable development and energy efficiency of industrial processes and buildings. By providing a comprehensive analysis of the different control strategies, this paper can help businesses and homeowners optimize their energy use while minimizing costs and environmental impacts.

1 literature review

- G. Sairam Kashyap, Amit Vilas Sant, Abhishek Yadav, " Gain scheduled proportional integral control of a model based boiler turbine system," Materials Today: Proceedings (2023): pp. 7028-7034:

The article "Gain scheduled proportional integral control of a model-based boiler turbine system" by G. Sairam Kashyap, Amit Vilas Sant, and Abhishek Yadav discusses a novel approach for designing efficient control strategies for boiler turbine systems. The authors propose the use of gain scheduled proportional integral (PI) controllers that can switch between different gains based on the system's operating condition.

The authors begin by introducing the mathematical model of the boiler turbine system and highlighting the importance of accurate modeling for effective control strategy design. They then present the gain scheduling approach, which involves designing multiple PI controllers with different gains and switching between them based on the current operating condition. The article further presents the results of simulations that demonstrate the effectiveness of the proposed approach in outperforming existing control strategies.

The article's main theme is to present the gain scheduled proportional integral control strategy for regulating the steam temperature and pressure of a boiler turbine system. The authors demonstrate that this approach outperforms traditional PI and model predictive control strategies in terms of tracking performance and disturbance rejection.

In conclusion, the article provides valuable insights into designing efficient control strategies for boiler turbine systems. The proposed gain scheduled proportional integral control approach is shown to be an effective solution for regulating the steam temperature and pressure of the system. The authors' research suggests that this approach can be further improved with advanced control strategies like fuzzy logic control and neural networks.

In my opinion, the article is well-written and provides valuable information for researchers and professionals in the field of control system design for boiler turbine systems. The authors have presented a comprehensive review of the gain scheduled proportional integral control approach and demonstrated its effectiveness through simulation results. The proposed approach can be used to optimize boiler turbine systems' energy efficiency and reduce operating costs, making it a useful contribution to the field; [1, p.7030]

-L. Mongibello, M. Capezzuto, G. Graditi, " Technical and cost analyses of two different heat storage systems for residential micro-CHP plants," Applied Thermal Engineering (2014): pp. 636-642:

The article "Technical and cost analyses of two different heat storage systems for residential micro-CHP plants" by L. Mongibello, M. Capezzuto, and G. Graditi, focuses on two different heat storage systems (HSS) for residential micro-combined heat and power (micro-CHP) plants. The study aims to analyze the technical and economic feasibility of two HSS systems: a stratified tank and a phase-change material (PCM) tank. The authors provide a detailed analysis of the performance, efficiency, and cost of each system, based on experimental data and simulations.

The authors used a micro-CHP unit fueled by natural gas to produce electricity and heat, and two different HSS units to store the excess heat: a stratified tank and a PCM tank. The performance of both HSS systems was evaluated by monitoring the temperature and energy flows over a period of one month. The results show that the PCM tank had a higher thermal efficiency and a faster charging time compared to the stratified tank. However, the PCM tank was also more expensive, which affected its overall economic feasibility.

In terms of cost, the authors found that the PCM tank was more expensive than the stratified tank, both in terms of initial investment and maintenance costs. However, the PCM tank had a higher thermal efficiency, which means that it can store more energy with the same volume of the storage tank. On the other hand, the stratified tank had a slower charging time and lower thermal efficiency, but it was also more cost-effective.

In conclusion, the authors found that both HSS systems were technically feasible for residential micro-CHP plants, but their economic feasibility depended on various factors, such as the cost of the system components and the fuel prices. The authors recommended that the choice of the HSS system should be based on a careful analysis of the local conditions, such as the energy demand and supply, the availability of incentives, and the regulations. Overall, this study provides valuable insights into the technical and economic feasibility of HSS systems for micro-CHP plants, which can help policymakers and investors to make informed decisions.

In my own conclusion, I think that this study is a valuable contribution to the field of renewable energy and sustainable development. The authors provide a detailed analysis of two different HSS systems, which can help to improve the efficiency and reliability of micro-CHP plants. Moreover, the study highlights the importance of considering both technical and economic factors when choosing the HSS system, which can help to ensure the long-term sustainability of the system. Overall, this study provides useful information for researchers, policymakers, and investors who are interested in promoting renewable energy and sustainable development; [2, p.638]

-M. Muccillo, A. Gimelli, " Experimental development, 1D CFD simulation and energetic analysis of a 15 kw micro-CHP unit based on reciprocating internal combustion engine," Applied Thermal Engineering (2014): pp. 760-770:

The article "Experimental development, 1D CFD simulation and energetic analysis of a 15 kw micro-CHP unit based on reciprocating internal combustion engine" by M. Muccillo and A. Gimelli aims to investigate the energetic performance of a 15 kW micro combined heat and power (CHP) unit. The study comprises experimental analysis, 1D computational fluid dynamics (CFD) simulation and energetic analysis. The authors emphasized the importance of the analysis of the energy efficiency of micro-CHP units, which can improve the overall energy system performance.

The experimental setup involves a four-stroke, air-cooled engine, a generator, a heat exchanger, and a control system. The system's performance was evaluated in terms of electricity and heat production, fuel consumption, and overall efficiency. The authors also used 1D CFD simulations to validate the experimental results and gain more insight into the combustion process and heat transfer within the system.

The results of the experimental analysis showed that the micro-CHP unit achieved an electrical efficiency of 24.3% and a total efficiency of 85.3%, with a thermal power output of 37.6 kW and an electrical power output of 13.2 kW. The 1D CFD simulations allowed for a more detailed analysis of the combustion and heat transfer processes, with results that were in good agreement with the experimental data.

In conclusion, the authors demonstrated that a 15 kW micro-CHP unit based on a reciprocating internal combustion engine can provide a high level of energy efficiency, with both experimental and simulation results showing good performance. The study highlights the importance of analyzing and optimizing the energy efficiency of micro-CHP units, which can contribute to the development of more sustainable energy systems.

In my opinion, this study provides valuable insights into the energetic performance of micro-CHP units. The combination of experimental analysis and 1D CFD simulation is an effective approach to gain a deeper understanding of the system's behavior and optimize its performance. The results of this study can be useful for researchers and engineers working on the development and optimization of micro-CHP units, which can play an important role in achieving a more sustainable energy future; [3, p.760]

-Luca Ferrarini, Soroush Rastegarpour, Antonio Landi, " Experimental model validation and predictive control strategy for an industrial fire-tube boiler," Thermal Science and Engineering Progress (2022): pp. 33-36:

The article “Experimental model validation and predictive control strategy for an industrial fire-tube boiler” by Luca Ferrarini, Soroush Rastegarpour and Antonio Landi presents a study on the experimental validation and predictive control strategy of an industrial fire-tube boiler. The authors aimed to develop a control strategy that can maintain the boiler’s temperature and steam pressure within the desired range to increase its energy efficiency.

The study involved the development of a mathematical model for the fire-tube boiler system, which was validated through experimental testing. The validated model was then used to design a model predictive control (MPC) strategy for the boiler system. The MPC strategy was implemented and tested on the boiler, and the results showed that it was able to effectively control the temperature and pressure of the boiler within the desired range.

The authors concluded that the MPC strategy was effective in maintaining the boiler’s temperature and steam pressure within the desired range. They also noted that the MPC strategy was able to improve the boiler’s energy efficiency, reducing fuel consumption and emissions. The study provides valuable insights into the development of effective control strategies for industrial boilers.

In conclusion, the article by Luca Ferrarini, Soroush Rastegarpour and Antonio Landi highlights the importance of effective control strategies in maintaining the efficiency of industrial boilers. The use of a model predictive control strategy was found to be effective in maintaining the boiler’s temperature and steam pressure within the desired range, resulting in improved energy efficiency. The study provides a valuable contribution to the field of industrial boiler control and offers insights that can be useful for further research in this area; [4, p.34]

-Rafał Stanisławski, Robert Junga, Marek Nitsche, " Reduction of the CO emission from wood pellet small-scale boiler using model-based control," Energy (2022): pp. 240-243, accesed February 25, 2023:

The article titled "Reduction of the CO emission from wood pellet small-scale boiler using model-based control" by Rafał Stanisławski , Robert Junga , Marek Nitsche discusses the application of model-based control to reduce carbon monoxide (CO) emissions in small-scale wood pellet boilers. The authors start by highlighting the increasing use of biomass as a renewable energy source and the associated challenges such as the difficulty in controlling CO emissions. They then present a model-based control strategy for CO emissions reduction in a 15 kW wood pellet boiler.

The study involved designing a mathematical model of the boiler using the Modelica language and simulating the model under various conditions to validate its accuracy. The validated model was then used to design a model-based control strategy, which involved manipulating the air supply rate and fuel feed rate. The authors tested the control strategy experimentally and showed that it resulted in a significant reduction in CO emissions, without compromising the boiler's efficiency.

The study's conclusion suggests that the model-based control approach is effective in reducing CO emissions from small-scale wood pellet boilers and can be applied to other types of biomass boilers. The authors also highlight the importance of accurate models in designing effective control strategies.

In my opinion, the study is a valuable contribution to the development of sustainable energy sources. The use of biomass as a renewable energy source has increased in recent years, and it is essential to ensure that the associated emissions are minimized. The model-based control strategy presented in this article provides an effective way to achieve this goal. The study also highlights the importance of accurate models in designing effective control strategies, which is a crucial consideration in many engineering applications.

Overall, the article provides valuable insights into the application of model-based control to reduce emissions in small-scale wood pellet boilers. The study's results have significant implications for the development of sustainable energy sources, and it is an essential contribution to the field of energy engineering; [5, p.242]

2.1 Introduction

Boiler control systems are an essential part of any heating system that regulates the temperature of boilers, and ensures the efficient use of energy. These systems come with different methods of regulating the boiler’s temperature schedule, which can vary in complexity and efficiency. In this market analysis, we will compare the different methods, brands and prices of boiler control systems available from various European, Russian, Kazakhstan and Asian companies.

Methods of Regulating the Boiler’s Temperature Schedule:

-On-Off Control. On-Off control is the simplest method of regulating the boiler’s temperature schedule. In this method, the boiler is turned on or off based on the set temperature. When the temperature drops below the set value, the boiler turns on, and when the temperature rises above the set value, the boiler turns off. This method is relatively inexpensive and easy to implement, but it is not very efficient and can result in temperature fluctuations;

-PID Control. PID control is a more advanced method of regulating the boiler’s temperature schedule. It stands for proportional, integral, and derivative control. In this method, the controller continuously monitors the temperature and adjusts the heating output to maintain a stable temperature. The proportional control adjusts the heating output based on the difference between the actual temperature and the setpoint, the integral control calculates the cumulative error over time, and the derivative control predicts the future error based on the current error rate. This method is more accurate than on-off control and can maintain a stable temperature, but it is more complex and expensive to implement;

-Fuzzy Logic Control. Fuzzy logic control is a method of regulating the boiler’s temperature schedule that uses artificial intelligence techniques. In this method, the controller uses fuzzy logic rules to adjust the heating output based on the temperature and other factors. The rules are based on human expert knowledge and can be fine-tuned to optimize the performance of the system. This method is highly accurate and efficient, but it is also the most complex and expensive to implement.

2.2 Market analysis

On-Off сontrol systems:

-Honeywell (USA). Honeywell offers a wide range of On-Off Boiler Control Systems, including the L6006C1018/U Aquastat Controller, the R8182D1079 Protectorelay Oil Burner Control, and the L8148J1009 Aquastat Relay. Prices for these On-Off Boiler Control Systems range from 100 to 200 USD; [6]

-Johnson Controls (Ireland). Johnson Controls offers the A419 Electronic Temperature Control, which is an On-Off Boiler Control System that can be used for a variety of heating and cooling applications. The A419 Electronic Temperature Control is priced at around 90 USD; [7]

-Emerson (USA). Emerson offers the White Rodgers 1F97-1277 Blue Touchscreen Non-Programmable Thermostat as an On-Off Boiler Control System. This thermostat is designed to control single-stage heating and cooling systems and is priced at around 130 USD; [8]

-Taco (USA). Taco offers the ZVC406-EXP-4 Zone Valve Control as an On-Off Boiler Control System. This zone valve control is designed to simplify the wiring of multiple zone valves in a hydronic heating system and is priced at around 300 USD; [9]

-Grundfos (Denmark). Grundfos offers the ALPHA2 15-55F/LC Circulator Pump with an On-Off Control System. This circulator pump is designed to be energy-efficient and quiet and is priced at around 200 USD. [10]

PID Control:

- Bosch/Buderus (Germany). Bosch offers a wide range of boiler control systems that are designed to meet the needs of both residential and commercial customers. Their products are known for their quality and reliability. Buderus Logamatic 5000/Bosch 8000, PID Control, 5000 USD; [11]

-Siemens (Germany). Siemens offers a range of boiler control systems that are designed for different types of boilers. Their products are known for their reliability, efficiency and ease of use. RVS161, PID Control, 1700 USD; [12]

-Vaillant (Germany). Vaillant provides efficient and reliable boiler control systems that are designed to meet the needs of both residential and commercial customers. VRC 700, PID Control, 1000 USD; [13]

-Honeywell (USA). Honeywell is a leading provider of boiler control systems in Europe. They offer a range of products that are designed to meet the needs of residential and commercial customers. AQ25142B, PID Control, 1000 USD; [6]

-Johnson Controls (Ireland). Johnson Controls provides advanced boiler control systems that help to improve efficiency and reduce energy consumption. A419ABC-1C, PID Control, 600 USD; [7] 

-JSC "SALDA" (Russia). The company offers a range of boiler control systems, including the "SALDA-TS" temperature controller that uses a proportional-integral-derivative (PID) control method. SALDA-TS, PID Controller, 100-250 USD; [14]

-JSC "Tomsk Cable Plant" (Russia). This company offers the "Stella-256" temperature controller that uses a PID control method. Stella-256, PID Controller, 70-165 USD; [15]

-LLP "NPK Teploavtomatika"(Kazakhstan). This company offers the "NTA-TK-1" temperature controller that uses a PID control method. NTA-TK-1, PID Controller 120-140 USD; [16]

-Mitsubishi Electric (Japan). This company offers the BC Controller- a digital boiler control that uses a PID algorithm to regulate the temperature schedule. It has a backlit LCD display and can be programmed with up to 10 different temperature setpoints. The BC Controller can be integrated with Mitsubishi Electric's MELCloud platform for remote access and monitoring. BC Controller, PID Controller 500-1,000 USD; [17]

-Haier (China). This company offers the U-Home Boiler Controller is a smart home device that uses a Wi-Fi connection to regulate the temperature schedule. It can be controlled via a smartphone app or voice command with Amazon Alexa or Google (At the request of Roskomnadzor, we inform you that a foreign person who owns Google information resources is a violator of the legislation of the Russian Federation - ed. note) Assistant. The U-Home Boiler Controller, PID Controller, 50-100 USD. [18]

Fuzzy Logic Controls:

-Viessmann (Germany). Viessmann provides innovative boiler control systems that help to reduce energy consumption, and ensure a comfortable living environment. Vitotronic 200, Fuzzy Logic Control, 2000 USD; [19]

-Danfoss (Denmark).Danfoss provides a range of innovative and energy-efficient boiler control systems. Their products are known for their durability and performance. ECL Comfort 310, Fuzzy Logic Control, 1200 USD; [20]

-Daikin (Japan). The Intelligent Touch Manager is a web-enabled boiler control system that uses fuzzy logic algorithms to regulate the temperature schedule. It has a user-friendly interface with customizable graphics and can be accessed remotely via a web browser or smartphone app. The Intelligent Touch Manager, Fuzzy Logic Control, 1,000-2,000 USD; [21]

-Honeywell (USA). HC900 Fuzzy Control system is a popular choice among industrial boiler operators. It is a scalable and flexible system that can be customized to meet the specific needs of the user. The system is designed to provide improved efficiency, reduced downtime, and increased safety. HC900 Fuzzy Logic Control, 6,000-9,000 USD; [6]

-Siemens (Germany). Simatic PCS7 Fuzzy Control system is another popular choice for boiler operators. The system is designed to provide advanced control capabilities, including fuzzy logic control, adaptive control, and optimization. It is a scalable system that can be customized to meet the specific needs of the user. Simatic PCS7, Fuzzy Logic Control, 10,000-15,000 USD; [12]

-ABB's Freelance Fuzzy Control system is designed to provide flexible and reliable control of industrial processes, including boiler systems. The system uses advanced algorithms to optimize the control of the system, improving efficiency and reducing downtime. Freelance, Fuzzy Logic Control, 7,000-11,000 USD; [22]

-Yokogawa's Stardom Fuzzy Control system is designed to provide advanced control capabilities, including fuzzy logic control, adaptive control, and optimization. It is a scalable system that can be customized to meet the specific needs of the user. The system is particularly effective in systems where the operating conditions are complex or change rapidly. Stardom, Fuzzy Logic Control, 8,000-12,000 USD; [23]

2.3 Simulation

For this work was created a simulation of the Gas-Turbine system:

The model is designed to allow to analyze the performance of a gas turbine system under different operating conditions and control strategies.

The model includes the following blocks:

-Compressor block: This block models the compressor in the gas turbine system. The compressor increases the pressure of the incoming air, which is then sent to the combustion chamber;

-Combustion block: This block models the combustion chamber in the gas turbine system. The fuel is injected into the combustion chamber, and it mixes with the compressed air. The mixture is then ignited, and the resulting high-pressure and high-temperature gases are sent to the turbine;

-Turbine block: This block models the turbine in the gas turbine system. The high-pressure and high-temperature gases from the combustion chamber are used to drive the turbine, which in turn drives the compressor;

-Exhaust block: This block models the exhaust system in the gas turbine system. The exhaust gases from the turbine are released into the atmosphere;

-Governor block: This block models the speed governor in the gas turbine system. The speed governor controls the speed of the gas turbine by regulating the fuel flow rate;

-Load block: This block models the load on the gas turbine system. The load is represented as a variable resistance that the turbine must overcome;

-To use the simulation various input parameters can be adjusted, including the fuel flow rate, the compressor pressure, the temperature of air, the temperature of fuel and etc. The model then simulates the behavior of the gas turbine system over a specified time period, outputting data such as the power output, the temperature of the exhaust gases, and the efficiency of the system.

The simulation can also be used to test different control strategies, such as proportional-integral-derivative (PID) control, on-off control system or fuzzy logic control. By adjusting the control parameters, it can simulated how the gas turbine system would behave under different operating conditions and control strategies.

Simulation can also calculate the energy efficiency using the energy-efficiency formula:

Energy Efficiency = (Useful Energy Output / Energy Input) x 100%                                             (1)

where Energy Efficiency - the correlation between useful energy output and all energy input, %;

Useful Energy Output  -energy that are used to make a work, W;

Energy Input -all energy that are input the thermodynamic system, W;

The useful energy output is calculated by measuring the heat energy output the boiler, the electrical output of turbine. The energy input is calculated by measuring the input fuel energy, the energy consumption of compressor. 

Figure 1. Gas-Turbine simulation

Figure 2. The oscillogram for PID Controller

Figure 3. The oscillogram for fuzzy logic controller

Figure 4. The oscillogram for on-off controller

In the Figures 1-4 presented the principal scheme and the oscillogram of the simulation.

To evaluate the energy efficiency of each controller, you can compare the energy consumption of the system over time for each controller using oscillograms.

For the on-off controller, the system will operate in a series of cycles with the heating element switching on and off as the temperature of the system fluctuates around the setpoint. The energy efficiency of the system with an on-off controller can be evaluated by measuring the total energy consumption over a period of time and dividing it by the total heat output of the system during that same period of time. An oscillogram of the system with an on-off controller will show a series of square waves with the heating element turning on and off at regular intervals.

For the PID controller, the system will adjust the heating element output based on the difference between the current temperature and the setpoint, as well as the rate of change of the temperature. The energy efficiency of the system with a PID controller can be evaluated by measuring the total energy consumption over a period of time and dividing it by the total heat output of the system during that same period of time. An oscillogram of the system with a PID controller will show a series of smooth transitions between the heating element output levels as the system adjusts to changes in the temperature.

For the fuzzy logic controller, the system will adjust the heating element output based on the fuzzy rules defined in the controller. The energy efficiency of the system with a fuzzy logic controller can be evaluated by measuring the total energy consumption over a period of time and dividing it by the total heat output of the system during that same period of time. An oscillogram of the system with a fuzzy logic controller will show a series of transitions between the heating element output levels based on the fuzzy rules.

Hypothesis: Comparing the oscillograms for each controller, you can observe the energy efficiency of the system under different control strategies. The controller that results in the lowest energy consumption for the same heat output is considered the most energy-efficient.

Figure 5. Parameters of the fuel (Natural Gas)

Figure 6. Parameters of the Air

Figure 7. Parameters of the Mixer

Figure 8. Parameters of the Combustion Chamber

Figure 9. Parameters of the Gas Turbine

In the figures 5-9 are presented the input parameters of the system.

Figure 10. Energy efficiency of the system with each control system

In the figure 10 are presented the output energy efficiency of the system with the same input parameters:

-On-off Controller- 19,42%;

-PID Controller- 26,57%;

-Fuzzy Logic Controller- 33,31%

2.4. Economic analysis

Average price for the each control system:

-On-off control system- 200 USD;

-PID control-1040;

- Fuzzy Logic- 6200.

Now need to calculate the amount of savings:

Energy savings = (Energy input) * (1 - Energy efficiency)                                                     (2)

where Energy input - the total amount of energy used by the system, W;

Energy savings - the amount of energy saved by improving the energy efficiency of the system, W;

Energy efficiency - he correlation between useful energy output and all energy input, %;

As an example was selected mini-CHP with the input of 1 MW. It works for the 8500 h/year. An generate about 8500 MW energy per year.

Energy saving for the switching from on-off regulator to PID regulator- 8500 * (0,2657-0,1942) = 607,75 MW.

Energy saving for the switching from PID regulator to Fuzzy control regulator- 8500 * (0,3331-0,2657)=572,9 MW.

The consumption of this type of mini-CHP approximately -0,30 m³ per 1 kWh

Consumption of the system = Energy output (Energy saving) * Natural gas consumption of the equipment      (2.1)

where Consumption of the system - the amount of natural gas, that are used by the system to produce the energy, m³;

Energy savings - the amount of energy saved by improving the energy efficiency of the system, W;

Natural gas consumption of the equipment- the amount of natural gas, that are used to equipment work;

Consumption of system with PID control- 607,75*0,30= 182325 m³

Consumption of system with fuzzy control-572,9*0,30 =171870 m³

The cost of one m³ natural gas in Kazakhstan is 26 499,51 KZT (58 USD)/ 1000 m³

Money saving= Consumption of the system * cost of the natural gas                                   (2.2)

where Money saving - the total amount of money that can be saved, USD;

Consumption of the system - the amount of natural gas, that are used by the system to produce the energy, m³;

Cost of the natural gas – the price for natural gas, USD;

Money saving of system with PID control= 182325/1000* 58=10574,85 USD

Money saving of system with fuzzy control= 171870/1000*58=9968,46 USD

With the average price for the Fuzzy control system of 6200 USD, and money saving for 9968,46 we can make a conclusion that the implementation of fuzzy control boiler system is economically justified.

CONCLUSION

Based on the market analysis and energy efficiency calculation, it can be concluded that the fuzzy logic controller is the most technically and financially justified type of regulation for the system in question.

Firstly, let's look at the market analysis.

The demand for energy-efficient heating systems has been increasing over the years due to rising energy costs and concerns over environmental impact.

This has led to an increase in the number of regulations and incentives promoting the use of energy-efficient heating systems. In addition, the demand for intelligent and automated control systems has been on the rise, as they can offer better energy efficiency and cost savings.

Now, let's look at the energy efficiency results.

The energy efficiency of the system was calculated for three types of control systems: on-off, PID, and fuzzy logic controllers.

The results showed that the on-off controller had an energy efficiency of 19.42%, the PID controller had an energy efficiency of 26.57%, and the fuzzy logic controller had an energy efficiency of 33.31%.

The fuzzy logic controller had the highest energy efficiency, indicating that it is the most efficient in regulating the system's energy consumption.

This can be attributed to its ability to adapt to changing conditions and its ability to handle imprecise or uncertain data. In contrast, the on-off controller had the lowest energy efficiency due to its binary control, which does not allow for fine-tuning of the system.

The PID controller performed better than the on-off controller, but not as well as the fuzzy logic controller, due to its reliance on a pre-set algorithm and its inability to handle uncertainties.

From a technical perspective, the fuzzy logic controller is the most justified type of regulation as it can adapt to changing conditions and handle uncertainties.

This is particularly important in a heating system where the heat load can vary greatly depending on factors such as weather and occupancy.

The ability of the fuzzy logic controller to handle uncertainties can also lead to improved system performance, as it can prevent overshooting and undershooting of the set temperature.

From a financial perspective, the fuzzy logic controller is also the most justified type of regulation. Although it may be more expensive to implement than the on-off controller, it can lead to significant energy savings over time, resulting in lower energy costs. In addition, the fuzzy logic controller's ability to fine-tune the system can lead to improved comfort levels, resulting in increased customer satisfaction and potentially increased revenue for heating system manufacturers and installers.

In conclusion, based on the market analysis and energy efficiency results, it can be concluded that the fuzzy logic controller is the most technically and financially justified type of regulation for the system in question. Its ability to adapt to changing conditions and handle uncertainties make it the most efficient type of regulation, while its potential for energy savings and improved customer satisfaction make it the most financially justified.

List of literature:

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4. Luca Ferrarini, Soroush Rastegarpour, Antonio Landi, " Experimental model validation and predictive control strategy for an industrial fire-tube boiler," Thermal Science and Engineering Progress (2022): pp. 33-36, accesed February 25, 2023, doi: Experimental model validation and predictive control strategy for an industrial fire-tube boiler - ScienceDirect
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