Rotary drying process modeling and online compensation

Rotary drying process modeling is a complex procedure due to the difficulties in measurement and estimation of kinetic model parameters. To solve the problem, a hybrid modeling method with online compensation is proposed in the present study. A mathematical model is built to describe the axial characteristics of rotary drying process. The drying rate which is the key parameter in the model is estimated by using a SVR-based fuzzy modeling approach, which can automatically extract fuzzy IF-THEN rules from support vectors. Laboratory experiments are conducted to obtain the drying rate sample data for the modeling purpose. In order to reduce the modeling errors for an industrial rotary dryer and improve the hybrid model prediction accuracy, an online matching coefficient is introduced, and a method based on improved online SVR is then applied for modeling error compensation. The experiment dada based modeling results have verified the effectiveness and demonstrated the accuracy and adaptability of the proposed hybrid modeling method.     

Real-Time Monitoring of Gas–Solid Fluidized-Bed Granulation and Coating Process: Evolution of Particle Size,Fluidization Regime Transitions,and Psychometric Parameters

Moisture content and particle size are critical parameters in fluidized-bed granulation and coating. In this study, microcrystalline cellulose was granulated and coated. The particle size was monitored in real time by a Parsum IPP70 probe. The fluidization regime was assessed by Gaussian spectral analysis. The bed temperature and gas humidity profile were monitored to evaluate the drying efficiency. The defluidization phase was detected in the early stages with frequencies below 6.0 Hz. The Parsum probe showed the growth of the particles due to agglomeration. The psychometric parameters indicated the lower potential energy for drying. Therefore, monitoring in real time is important to detect failures.     

超细粉体抑爆装置瞬态喷射特征参数

在敞开空间开展了容积为2 L及5 L的超细粉体抑爆装置瞬态喷射参数研究，重点介绍了5种典型装配条件下的喷射性能，并对相关特征参数进行定量分析。结果表明：抑爆剂喷射所形成的超细粉体云幕特性由气体发生器装配结构和膜片开启压力共同决定，但两者之间必须恰当配置，作用在膜片上的能量份额较大时，膜片开启状态恶化，抑爆器腔体聚压不足，抑爆剂二次喷射严重，反之膜片不能正常开启。装配优化后的I型抑爆器在触发后10 ms内腔体聚压达到峰值，膜片破裂后5 ms内抑爆剂全部喷射，喷射动态性能较好，在第1个100 ms内扩散速度达到15 m/s，在第2个100 ms内扩散速度约13 m/s，0~1 000 ms内平均扩散速度约8 m/s，扩散过程较为连续，不存在二次喷射现象，喷射距离可达到7.5 m，抑爆剂质量分布呈渐进增长，产生的超细粉体云幕特征参数优良。Ⅱ型抑爆器两种装配的喷射表明：当抑爆装置中压力上升历程及压力峰值较为接近时，抑爆剂粉体喷射特性无显著差异。上述结论的可重复性得到了数百次喷射实验的验证。     

Boosting the photoconversion efficiency of TiO2 nanotubes using UV radiation-assisted anodization as a prospective method: An efficient photocatalyst for eliminating resistant organic pollutants

1D TiO₂ nanotube arrays (TNTs), as versatile nanostructures, have attracted a considerable amount of scientific attention, particularly in photocatalytic applications. In the present study, UV radiation-assisted anodization method with various irradiation times (30–120 min) was employed as a preferable approach to fabricating TNTs with remarkable optical property and photocatalytic activity. The results revealed that in situ irradiation not only improved the surface area (from 30.10 to 48.5 m²), but also increased the roughness factor (from 77.27 to 124.73). Furthermore, UV radiation had a significant impact on optical property and by altering elemental composition, led to a red shift in absorption edge (from 3.2 to 1.4eV). Meanwhile, voltammetric experiments showed that 120 min UV radiation during anodization was able to substantially cause a surge of the photocurrent density and the photoconversion efficiency of TNTs from 0.15 to 0.55 mA cm⁻² and from 13% to 40%, respectively. As a consequence of the improvement in optical property and photochemical features, anodic TNTs fabricated under 120 min UV radiation could increase the photocatalytic degradation of 2,4-DCP from 75% to 100%. Moreover, the kinetics study showed that all photocatalytic reactions followed zero-order kinetics which rate constant over the synthesized TNTs under 120 min UV radiation was about 5.1 times greater than that of conventionally fabricated TNTs. Likewise, the pathway of photocatalytic degradation and the proportion of reactive species in this process were assessed by scavenging tests. The results confirmed that holes (h⁺) play the main role that 53% of photocatalytic degradation occurred via both direct and indirect reactions with h⁺ species. The rest of the degradation pathways were also allocated to e⁻ and ^•O₂⁻ species by accounting for 37% and 10%, respectively.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

柴油机喷油器结构参数与磨料流加工效果关联的研究

基于磨蚀模型和多相流离散相模型建立了柴油机喷油器磨料流加工过程的CFD计算模型。在验证基础模型的有效性后,分别针对喷孔直径、喷孔锥角和交点位置三种结构参数进行变参数模拟计算。通过对加工域颗粒流速、湍流强度和磨蚀率的分析,获得了喷油器主要结构参数对液力研磨加工的影响机理:喷孔直径和喷雾锥度的变化对喷孔壁面磨蚀量的影响较大,随着两者的增大,喷孔壁面的磨蚀量有所增加;交点位置由上至下的变化对喷孔壁面磨蚀量的影响较小,但是喷孔入口处磨蚀量有所增加。     

Robust method to provide exponential convergence of model parameters solving linear time-invariant plant identification problem

The scope of this research is a problem of parameters identification of a linear time-invariant plant, which (1) input signal is not frequency-rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.