Fault Detection And Diagnosis In Gas-Solid Reactors Based On Acoustic Emission Signals

Process industry is one of the most important mainstay industries in our national economy, and multiphase flow reactors even play an important role in the continuous production within process industry.The accurate on-line faults detection and diagnosis of the reactors is an indispensable technique which is used to ensure the stable operation of the industrial plants. With the development of chemical engineering and modern scientific technology, the design of the multiphase flow reactors is evolving to the direction of multifunction, highly integrated and automation. The followed problem is that the hydrodynamic behaviors in the reactors turn to be much more complex, which causes big troubles on the on-line detection and diagnosis for the multiphase flow reactors. On the other hand, the traditional detection means, such as temperature and pressure, were only designed to record the process parameters, and could not meet the high standards (for examples, sensitive, accurate, and early warning) for the faults detection in modern industry. Therefore, in order to address these issues, it is not only challenging, but with practical and theoretical significance to develop a novel measurement tool and systematically investigate the mechanisms, characteristics, factors and corresponding strategies for the faults detection and diagnosis for the multiphase flow reactors.Based on the advanced non-invasive acoustic emission technique, combining the experimental study and theoretical analysis, this thesis had an in-depth study of the faults (agglomeration, coking) which were generated because of the qualities changing of particles (size, composition) in the three main multiphase flow reactors in chemical production, such as horizontal stirred bed reactors for polypropylene, fluidized beds for polyethylene and circulating fluidized beds for fluid catalytic carking. From this study, we not only established the new methods and diagnosis models based on the acoustic signals, but also proposed several corresponding means for the individual faults, which had an important guiding significance on the safely continuous production, optimization of the process and development of the new products. The results in this thesis can be summarized as following.1. After the in-depth research of the hydrodynamic information changes between the state that the particles quality had changed and the state in normal operation, we built up an Acoustic Emission-Early Agglomeration Recognition System, AE-EARS, for the chunks and large agglomerates detection and an on-line quantitative detection model for the amount of coke deposit on catalyst particles.1) AE-EARS, which was based on the nonlinear dynamic analysis and modern statistical theory, was composed with the statictical value S based on the attractor comparison method and the malfunction coefficients C. The inspiration of the system was from the idea of states comparison. It was based on the finding or calculating the differences between the evaluated state and the normal state, then the system could decide the current process conditions whether or not had changed.2) The attractor comparison method was based on a general distance concept between multidimensional distributions. By comparing the attractor of a reference AE time series at the normal operating conditions with the attractor of evaluated AE time series acquired during operation of HSBR, we were able to get the "distance" (which was called S value in a statistical way) between the two distributions. When the S value was close to zero, under the null hypothesis, it means that evaluated situation is similar to the normal operating condition; when the S value was larger than 3 the hydrodynamic behaviors in the evaluated situation had changed and agglomeration might have formed. 3) Based on the reconstruction of the attractors in multi-dimension space, correlation dimension and Kolmogorov entropy were introduced to monitor the dynamic process of malfunction in multiphase flow reactors.4) The on-line model for coke deposit measurement was established based on the improved power spectrum density, whose data transformed from the acoustics signals by FFT. The partial least square, PLS, method was used to quantitatively correlate the PSD data and coke deposit amount.2. By using the AE-EARS, we were able to give early warnings of agglomeration in both lad-scale and industrial horizontal stirred bed reactors (HSBR). Furthermore, by combining the detection results and the background of the production process, several corresponding means were proposed to resolve the particle problems.1) The parameters of this method for AE measurement were set by an optimization method. Based on the analysis of the general range of these four parameters, we had taken the biggest S values and the smallest stand deviations of S as optimization objective, and the S values increase with increasing fraction of PP4 as constraint function.2) External factors affecting the performance of the method had been reduced. A denoising algorithm based on a discrete wavelet transform was used to remove a part of noise from the agitator which made the method insensitive to small changes in the agitator speed. Normalization the AE signals before calculating the S values was able to remove most of the influence of the bed mass. By carefully choosing the positions for AE sensors could decrease the sensitivity of the S values to the air flow rate which means that a novel detection technique was absolutely necessary for selecting an optimal position for sensors during a lab-scale research. The allowable range for AE sensors was 1/8 D to 1/2 D above the bottom; and the optimal position for sensors was 1/4 D, where D was the diameter of the HSBR. 3) The AE technique based on the attractor comparison method was sensitive to small changes in the particle size distribution, which meant it could offer "early and accurate warning" of agglomeration in HSBR. Furthermore, the monitoring method might not only be used to indicate if a stationary situation had reached during a grade transition, but also had the probability to locate the agglomeration in HSBR with multiple-sensors. However, further research was needed before applying the method in an industrial HSBR for agglomeration detection.3. The performance of AE-EARS in the fluidized beds agglomeration detection had been investigated. According to the unique characteristics in fluidized state, we had optimized the output of AE-EARS by adjusting the parameters and reducing the false alarm rate.1) According to the theory that under heating conditions fine particles of polyethylene will adhere to each other to form larger chunks, lab-scale experiments were carried out to simulate the situation in real reactors. Meanwhile, the threshold functions for the parameters were obtained (aD2=0.3, aK2=1.2).2) The performance of the statistical S and malfunction coefficient C were compared in both lad-scale and industrial fluidized bed reactors. The results showed that whether on the sensitivity to agglomeration, or the timeliness to send warnings, the S method showed higher quality than C.3) Based on the "filtering" process for the original S, the accuracy of the proposed method was highly improved, from 95% to 99%.4. Based on the acoustic signals emitted by the collisions between the FCC particles and the internal wall of the circulating fluidized beds, supplemented with on-line quantitative detection model, the experiments both in lab-scale and industrial plants to measure the amount of coke deposit on catalyst particles were studied.1) Particle properties of the catalyst which had different amount of coke were characterized by several measurements. The particle size distribution showed no big changes between the fresh catalyst and the coked one. The BET tests told us that the surface area, pore volume and size had all dropped after the reaction. Therefore, it could be inferred that most of the coke deposited in the pores on the surface of the catalyst particles, and the distribution showed non-continuous status.2) A model to predict the amount of coke deposit on catalyst was established to describe the correlation between the characteristic frequency of AE signals from the particles-wall collisions and different amount of coke deposit. Experiments in a cold mode fluidized bed showed that this model was useful for online measurement of the amount of coke deposit on catalyst. The average absolute relative deviation (AARD) was below 8.12% when the predicted amount of coke deposit was compared with the true one.3) The correlation between acoustic power spectrum density and the amount of coke deposit were investigated in lab-scale experiments by PLS regressions method. The best PLSR results, obtained under the different superficial gas velocities showed for a correlation coefficient of 0.906, and a root mean square error of cross validation (RMSECV) of 5.35%, which fully met the requirements of industrial applications.4) The PLS model had been verified in the industrial plants, and the final results could be used to illustrated that the proposed method could meet the requirements of on-line measurement of the amount of coke deposit on catalyst in circulating fluidized bed reactors.