Research on Control Method of Waste Heat Utilization System

Research background

With the rapid development of society, people’s demand for energy is also increasing. At present, the global use of clean energy accounts for less than 18%, and the large-scale use of primary energy, especially fossil energy, is still the main energy lifeline of current industrial development. In the process of using fossil energy, on the one hand, it will cause pollution and damage to the environment. On the other hand, due to efficiency issues, a large part of the energy will be lost to the surrounding environment in the form of heat. Among the lost waste heat, part of it is easier to recover due to its higher temperature. Now many industries already have industrialized recovery methods, such as sinter waste heat recovery technology in the steel industry and steel slag waste heat recovery technology Etc.; screw expansion power machine power generation technology in the coking industry; low-temperature waste heat recovery power generation technology in the cement industry, etc. However, for the recovery and utilization of low-quality waste heat with a temperature not exceeding 160℃ and a pressure not exceeding 0.8 MPa, the above methods are difficult to effectively recover it, so a considerable part of the low-quality waste heat is wasted.

Industrial waste heat resources are widely distributed in many industries such as iron and steel, metallurgy, building materials, non-ferrous metals, petrochemicals, light industry, etc. It is currently a recyclable resource with the most widespread distribution and the greatest application potential in industrial production. Industrial waste heat is a kind of secondary energy. It is the heat lost in the industrial production process of primary energy. It is generally discharged into the external environment in the form of flue gas, waste gas, and wastewater1. According to statistics, the total amount of waste heat resources in the metallurgy, building materials and chemical industries is relatively large, reaching about 80%; medium and low-quality waste heat resources account for about 54%, and the annual utilization rate is about 2.7 million tons of standard coal2. As shown in Fig. 1, high-, medium-, and low-temperature waste heat accounted for 40%, 26%, and 34%, respectively, but their secondary utilization rates are quite different. Among them, the medium and low temperature waste heat is widely distributed, but due to its low quality, the recovery rate is much lower than the high temperature waste heat, which limits the further improvement of the overall utilization rate of industrial waste heat3. Research on low-quality waste heat recovery technology is conducive to comprehensive conservation and efficient use of resources, promote the development of low-carbon cycles, advance the energy revolution, accelerate energy technology innovation, and build a clean, low-carbon, safe and efficient modern energy system. Energy saving, emission reduction and environmental protection will be an important part of economic development in the future.

Figure 1
figure 1

Distribution and reuse of waste heat resources.

Literature review

At present, the research on low-quality waste heat recovery in universities and scientific research institutions mostly uses screw expanders and scroll expanders as core equipment, and most of the research on waste heat recovery is the improvement and optimization of existing solutions. However, these studies have obvious shortcomings when applied to low-quality waste heat recovery, which are mainly manifested in the complex structure of the core equipment, high processing costs, inconvenient maintenance, and high operating costs. As a result, these technologies and equipment are not widely used in low- and medium-quality waste heat utilization systems, and they cannot meet the needs of small and medium-sized enterprises for energy-saving technologies4. In addition, while the mechanical structure of waste heat recovery device is studied abroad, it is also gradually studied in the micro direction. On the one hand, the heat transfer effect can be improved by adding nano-particles or nano-fluids; on the other hand, the heat transfer efficiency can be improved by improving the radiator at the nano-level. Ibrahim Muhammad studied stretchable rotating discs with heat transfer functions and carried out numerical analysis of their fluids5,6. Zhixiong Chen et al. tested 27 refrigerants and studied a thermal conductivity model with better accuracy7. Subsequently, nano-particle fluids such as copper oxide or alumina were added into the heat transfer system, and thermodynamics laws and exergy were analyzed. The analysis results show that adding nanoparticles into the heat exchanger fluid can reduce exergy loss and reduce the efficiency of the second law of thermodynamics, so as to improve energy conversion efficiency8,9,10,11,12,13. This kind of method also plays a positive role in waste heat utilization technology, but also has the disadvantage of high cost.

In response to the technical requirements of low-quality waste heat recovery and utilization, the research group has developed a new type of Roots-type power machine and used it as the core equipment for waste heat recovery and utilization.It has been verified through experiments that it can be used for the recovery and utilization of low-quality waste heat. The device is shown in Fig. 2. At present, the waste heat recovery process of the device and its control system have been studied, but the existing research is not deep enough. Although the existing control method can solve the problem of the operation of the waste heat recovery device, it is difficult for the existing control method to return the system to the preset rated state at a faster speed and a smaller overshoot when the air source fluctuates. The fluctuation of the gas source will cause the output power to fluctuate. If this fluctuation is not controlled, it will cause the waste heat recovery device to become overloaded during energy conversion. In most cases, the recovered waste heat will be used for power generation, and when the waste heat recovery device is overloaded, the connected electrical equipment is bound to be affected. Therefore, this subject intends to study a control method to solve the problem that the low-quality waste heat recovery device cannot work stably when it is disturbed.

Figure 2
figure 2

Low-quality waste heat recovery and utilization device with roots power machine as the core.

The low-quality waste heat has small scale, frequent fluctuations, low specific heat capacity, and the fluctuation range is more severe than that of medium and high temperature waste heat, which makes it difficult to stabilize the operating state of the Roots waste heat power generation device. Irregularly fluctuating air source, the large inertia of the Roots power machine, the strong coupling effect of temperature and pressure and other parameters, coupled with the different time and location of the external environment, will cause the Roots waste heat power generation device to produce irregular deviations in the output power. When the deviation is severe, it may even cause partial load or generation trip. In order to make the Roots power machine run stably, it is necessary to decouple the variables that affect the rotation speed of the Roots power machine. With the decoupling model, the control effect of the control system will be more accurate and stable.

Traditional decoupling methods are mainly suitable for linear time-invariant multivariable systems. The basic idea of designing decoupling method is to build a decoupling network, and make the transfer function matrix between input and output variables become diagonal matrix, so that the system is easier to control. Adaptive decoupling control strategy is a combination of adaptive control technology and decoupling control technology, that is, the decoupling, control and identification of the controlled object is combined to achieve precise decoupling control of the system with unknown or time-varying variables. In essence, the coupling term can be regarded as measurable interference, and the coupling action, static compensation and compensator parameters can be optimized by self-correcting feedforward control method. Adaptive decoupling has been applied in many engineering fields, but its application scope is limited because of the need for online identification of target model, complex algorithm, large amount of calculation, poor adaptability to dynamic modeling and process disturbance, and weak robustness of the system14.

In the aspect of control system, the control system of low quality waste heat recovery and utilization system is mainly embedded control system. The embedded controller has many advantages such as small volume, high reliability, powerful function and easy to use. Zhang Wen et al. studied the dynamic performance of internal combustion engine—organic Rankine cycle combined system in the waste heat recovery system of internal combustion engine. The closed-loop proportional integration and feedforward control are adopted. The response time and overshoot of PI control are estimated and compared with that of feedforward control alone. The results based on the World Coordinated Transient cycle (WHTC) show that the designed closed-loop PI control has shorter response time and better tracking ability in the dynamic process15. Pang Kuo Cheng et al. constructed a 3 kW organic Rankine cycle test rig engineering simulator based on the experimental data of R245fa, R123 and their mixtures. The simulation performance of pump and expander is verified by experimental results, and the influence of mass flow rate is discussed. The results show that the proposed overheat control strategy can obtain the best operating conditions. Frequency conversion control strategy is preferred for small ORCS. It indicates that the organic Rankine cycle engineering simulator is a good tool to predict the operation characteristics of the organic Rankine cycle, and can further guide the advanced evaluation and long-term variation16. In the Rankine cycle technique, Toffolo proposed a hybrid evolution/traditional optimization algorithm, which considered the heat transfer constraints in the pipeline. Using his algorithm, a waste heat recovery system model with good tracking ability can be obtained17. Quoilin and Lemort et al. have modeled the organic Rankine cycle based on a vortex expander. Through this model, they confirmed that the organic Rankine cycle is particularly suitable for recovering low temperature waste heat, and also pointed out through experimental analysis that the main losses affecting the performance of expander are internal leakage, supply pressure drop to a lesser extent, and mechanical losses18,19,20. Jaume Fito, Sacha Hodencq et al. found that the waste heat temperature was correlated with the capacity of the heat storage device and optimized it to improve the waste heat recovery rate21. In the waste heat recovery system of power plant, the designer uses PLC as the controller and WinCC configuration software as the upper computer to develop the waste heat recovery monitoring system. In addition to controlling waste heat recovery, monitoring parameters can also be displayed in real time on the screen of the upper computer22. Designers optimize the traditional PID control method and develop a waste heat recovery control system based on fuzzy PID control strategy. More accurate control of parameters is achieved, and the energy consumption of the control system is also reduced23. In the diesel engine waste heat recovery system, the researchers used the MotoTron rapid prototyping development platform to better control the exhaust through an optimized PI closed-loop control strategy to achieve fuel savings. In addition, fault diagnosis and alarm functions are added to the system to monitor possible abnormal situations24. Zhao Mingru proposed a set of map-based feedback closed-loop control algorithm for the waste heat recovery system of internal combustion engine under driving conditions. Firstly, the model order reduction method was adopted to simplify the initial organic Rankine cycle model into a reduced order model that can be used for control without losing excessive accuracy. Then, the rolling time domain optimization is combined with the particle swarm optimization algorithm to form the nonlinear model predictive controller. Finally, the nonlinear state estimator constitutes the final feedback link, and the control effect is greatly improved25.

According to the summary above and the research on the control system of the existing waste heat recovery device, the current control strategy is mainly divided into the following aspects:

(1) PID control strategy.Now PID control strategy is the most widely used control method in the actual industrial production process. In the application of PID control strategy, the effect of PID control depends largely on the parameters of PID controller. In addition, PID control involves few controlled parameters and the signal processing process is relatively simple. Therefore, combined with the previous summary, most scholars adopt PID or improved PID control method in preliminary study of waste heat recovery control system26.

However, PID control method considers too few factors in the signal processing process, and there is no good method to generate differential signal. In traditional PID control strategy, the function of error integral feedback is to eliminate static error, so as to improve the accuracy of system response. At the same time, the closed-loop system becomes insensitive due to the introduction of system error integral feedback. When the traditional PID control method is applied to the low-quality waste heat recovery system, the system is prone to oscillations, which eventually leads to pulsating air flow in the pipeline27.

(2) Optimized PID control strategy. Due to the defects of traditional PID control strategy, the research on PID optimization is very rich. For example, the closed-loop PI control and fuzzy PID control mentioned above are optimized on the basis of PID control. The control signal of the traditional PID control strategy is directly obtained by the difference between the set value and the output feedback value, which leads to the contradiction between the rapidity of response and the overshoot. Active disturbance rejection technology is derived from the process of PID optimization28.

For the waste heat recovery power generation system, there are many variables in the system, which is a multi-input and multi-output system, and there are many variables associated with each other, with strong coupling. The optimized PID controller usually does not need an accurate mathematical model, but the coupling between different variables will lead to the reduction of the robustness of the controller in the adjustment process29.

(3) Decoupling control strategy. As the control system becomes more and more complex, the variables in the control system also become more and more, and the coupling between the variables in the control system becomes more and more prominent. Variable coupling is a common phenomenon in industrial control system. Because of the coupling between variables, it will not only increase the difficulty of industrial system control, but also greatly reduce the control effect of the system, and even lead to the collapse of the whole system in serious cases. Therefore, decoupling strategy in the control system has become one of the important means to improve the controller performance and meet the requirements of the control process.

Traditional decoupling methods are mainly suitable for linear time—invariant multivariable systems. The basic idea of variable decoupling design method is to construct a decoupling network, calculate the transfer function of multi-input and multi-output control system, and make its transfer function matrix into diagonal matrix, reduce the complexity of control system design.

Adaptive decoupling control strategy is a new control strategy derived from the integration of adaptive control technology and decoupling control technology, which combines the coupling analysis, control and identification of the controlled object to achieve more accurate control of the system containing unknown variables or time-varying systems. The essence of adaptive decoupling control technology is that coupling variables are regarded as measurable interference variables, and the coupling actions, static compensation and compensator parameters of the control system are optimized by the feedforward control method with self-correction function. There are many typical application examples of adaptive decoupling control in the field of engineering, but due to the need for online identification of target model, complex algorithm, large amount of calculation, poor adaptability to dynamic modeling and process disturbance and weak system robustness, the application scope is limited to a certain extent30.

(4) Intelligent algorithm fusion control strategy. In other words, control system design is carried out through artificial intelligence control algorithms, including pinch point technology, nonlinear programming, multi-integer linear programming, genetic algorithm, artificial neural network, multigeneration system and many other different methods31. Using self-learning artificial intelligence control algorithm can achieve more intelligent control effect, but with the gradual improvement of control effect, the complexity of control system design is getting higher and higher.

In addition, intelligent algorithms also have their own advantages and disadvantages. Therefore, the intelligent algorithm fusion control strategy utilizing the complementary advantages of different algorithms is gradually developed. In the study of waste heat recovery, Xiao Yanjun et al. found that Long Short-Term Memory(LSTM) often had high accuracy and sensitivity in the aspects of pre-processing, feature selection and data analysis, and had a strong ability to deal with strongly coupled data. Long and short term memory model can deal with a large amount of data effectively and improve the response speed of control system while predicting data. When internal model control is applied to large time delay systems, the control effect has obvious advantages compared with other control strategies, and has good tracking performance and anti-interference ability. Therefore, an algorithm fusion control strategy based on deep learning and internal model control is proposed32. Compared with the single control algorithm, the stability of the algorithm fusion control strategy has been significantly improved. Similarly, the complexity and development cycle of the system are several times higher than before.

To sum up, in order to improve the heat recovery efficiency of the waste heat recovery device and optimize the stability of the system, on the one hand, it is necessary to decoupled multiple variables with strong coupling in the system and analyze the relationship between different variables. On the other hand, combining the decoupling algorithm with the control algorithm to control the waste heat recovery system on the basis of decoupling can effectively improve control accuracy and stability.

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