Estimation of valve stiction in control loops using separable least-squares and global search algorithms

This contribution presents a new procedure for quantifying valve stiction in control loops based on global optimisation. Measurements of the controlled variable (PV) and controller output (OP) are used to estimate the parameters of a Hammerstein system, consisting of a connection of a two-parameter stiction model and a linear low-order process model. As the objective function is non-smooth, gradient-free optimisation algorithms, i.e., pattern search (PS) methods or genetic algorithms (GA), are used for fixing the global minimum of the parameters of the stiction model, subordinated with a least-squares estimator for identifying the linear model parameters. Some approaches for selecting the model structure of the linear model part are discussed. Results show that this novel optimisation-based technique recovers accurate and reliable estimates of the stiction model parameters, dead-band plus stick band (S) and slip jump (J), from normal (closed-loop) operating data for self-regulating and integrating processes. The robustness of the proposed approach was proven considering a range of test conditions including different process types, controller settings and measurement noise. Numerous simulation and industrial case studies are described to demonstrate the applicability of the presented techniques for different loops and for different amounts of stiction.     

Influence of mechanical alloying on Ti3SiC2 formation via spark plasma sintering technique from Ti/SiC/C powders

Ti₃SiC₂ was elaborated by two different methods: (i) Spark plasma sintering of 5Ti/2SiC/C powders and (ii) mechanical alloying of powders followed by Spark plasma sintering. The results showed that mechanical alloying was not advantageous for pure Ti₃SiC₂ formation but it can significantly improve the density of the obtained bulk material via the particles refinement as well as the microhardness by increasing the TiC content. It was found that the relative density was increased up to 98.58% for the sintered mechanically alloyed sample whereas it was not more than 96.04% for the sintered 5Ti/2SiC/C starting powders. The Vickers microhardness measured for both bulk samples demonstrates a high improvement for the previously mechanically alloyed powder mixture, as it was of about 1282 Hv and only 581.2 Hv for the alloy obtained from 5Ti/2SiC/C starting powders.     

EKO公司冷连轧机中带钢平直度的改善

轧制的带钢必须满足不断提高的平直度要求，这向平直度自动控制系统提出了一个无止境的难题。这些控制系统的目的是调节轧制过程，使得大量生产的产品保持在严格的公差范围内。     

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R² values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.     

Explicit models of thickness profile and tension stress distribution for process control applications

This contribution aims to present a compact and efficient model for the calculation of strip thickness profile and shape which refers to the tension stress distribution. The model is derived from appropriate combination and generalisation of analytical approaches well known in the published literature. The basis for the model is given from analytical solutions of the gaugemeter, roll force and tension stress differential equations linearised about the actual operating point. A novel profile and flatness prediction algorithm is developed which leads to more efficient solutions for cold and hot rolling mills. The resulting algorithm has substantial advantages over the traditional models in its efficient computation, and hence, provides an opportunity for real‐time implementation in advanced model based control systems.     

Performance assessment of control systems in rolling mills – application to strip thickness and flatness control

This contribution takes the control performance monitoring (CPM) technology into a new industrial area, the metal processing, where not much work has been done before. It is shown how to explore not yet discovered opportunities for controller performance improvement in this industrially interesting field, where special aspects have to be considered. This includes online vs. batch-wise performance evaluation, time-based vs. length-based assessment, and oscillation diagnosis. The paper presents two industrial case studies of the performance evaluation of control systems in a tandem cold rolling mill: (1) a feedforward/feedback strip thickness controller; and (2) an internal model control of the strip flatness. Routine operating data are analysed to obtain the performance monitoring metrics in terms of minimum variance index, oscillation index, and to suggest improvement measures. A monitoring tool for calculating and displaying the performance indices has been developed, and tailored to the applications in this field. The results indicate that tuning the feedback thickness controller is suggested to better handle entry thickness disturbances for specific coils. The performance of the flatness controller is found to be satisfactory, thus no actions are needed.     

Synthesis and characterization of nanostructured silicon carbide

Equiatomic nanostructured silicon carbide was successfully prepared by milling elemental Si and C powders, using a planetary ball mill. The synthesis of this carbide proceeded at milling conditions corresponding to 5.19 W/g shock power. The reaction was gradual and completed after 15 h. After 20 h of alloying duration and towards the end of the process, the SiC diffraction crystallite size (DCS) reached a critical value of 4 nm. At this same alloying duration, SEM characterization revealed that the powders exhibit homogeneous distribution of the particles with 0.3 µm in size.