工业共沸精馏塔软测量建模方法的研究与应用

针对某一工业共沸精馏塔成分估计问题,利用基于支持向量机技术的软测量建模方法,建立了恰当的工业软测量模型。利用滑动时间窗技术实时更新建模数据集,并根据预估精度决策在线优化和模型更新,提高工业软测量模型的在线估计精度。研究结果表明,基于滑动时间窗的LS SVM软测量建模方法,是一种有效的软测量建模方法。     

Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size

Soft sensors have been widely used in chemical plants to estimate process variables that are difficult to measure online. One crucial difficulty of soft sensors is that predictive accuracy drops due to changes in state of chemical plants. The predictive accuracy of traditional soft sensor models decreases when sudden process changes occur. However, an online support vector regression (OSVR) model with the time variable can adapt to rapid changes among process variables. One crucial problem is finding appropriate hyperparameters and window size, which means the numbers of data for the model construction, and thus, we discussed three methods to select hyperparameters based on predictive accuracy and computation time. The window size of the proposed method was discussed through simulation data and real industrial data analyses and the proposed method achieved high predictive accuracy when time-varying changes in process characteristics occurred.     

Development of a vision‐based online soft sensor for oil sands flotation using support vector regression and its application in the dynamic monitoring of bitumen extraction

Extraction from oil sands is a crucial step in the industrial recovery of bitumen. It is challenging to obtain online measurements of process outputs such as bitumen grade and recovery. Online measurements are a prerequisite for innovating better process control solutions for process efficiency and cost reduction. We have developed a soft sensor to provide online measurements of bitumen grade and recovery in a flotation‐based oil sand extraction process. Continuous froth images were captured using a VisioFroth camera system on a batch flotation unit. A support vector regression (SVR) model with a Gaussian kernel was constructed to develop a soft sensor for bitumen grade and recovery using froth image features as the inputs. The model was trained and validated for batch flotation of different grades of oil sands ore at industry‐relevant process conditions. A Dean‐Stark analyzer was used to obtain offline grade and recovery measurements that were used to calibrate the soft sensor. Mean squared errors (MSE) of 62 and 74 were achieved for grade (%) and recovery (%), respectively, and this was obtained using 5‐fold cross validation. The developed soft sensor model has been applied successfully in the real‐time dynamic monitoring of flotation grade and recovery for different grades of ore and operating conditions.

     

Mechanochemical approach for fabrication of a nano-structured NiO-sensing electrode used in a zirconia-based NO2 sensor

Highly uniform NiO nano-particles with a crystallite size of about 3 nm were obtained by room-temperature ball-milling of the parent Ni(OH)₂, which was derived using a sol-gel method. The obtained nano-structured NiO precursor was then utilized for the fabrication of NiO-sensing electrodes (SEs), which were further examined in the mixed-potential-type YSZ-based planar NO₂ sensor. The obtained results revealed the attractive advantages for the application of mechanochemical approach in regard to achieve high NO₂ sensitivity, NO₂ selectivity and reproducibility. All of the evaluated sensors attached with the nano-structured NiO-SEs, regardless of its sintering temperature, were found to exhibit high NO₂ sensitivity at 800 °C under the wet condition (5 vol.% water vapor). In addition to high NO₂ sensitivity, the sensor attached with 1100 °C-sintered NiO-SE showed highly selective properties towards NO₂. The observed improvement in NO₂-sensing characteristics as well as the attainment of highly reproducible behavior for different sensor devices is discussed based on morphological and electrochemical observations of the studied sensors.