Wyniki wyszukiwania - BazTech

1

Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate

Pural Yusuf Enes

Physicochemical Problems of Mineral Processing

|

2023

|

Vol. 59, iss. 5

art. no. 169823

EN

Soft sensors are mathematical models that estimate the value of a process variable that is difficult or expensive to measure directly. They can be based on first principle models, data-based models, or a combination of both. These models are increasingly used in mineral processing to estimate and optimize important performance parameters such as mill load, mineral grades, and particle size. This study investigates the development of a data-driven soft sensor to predict the silicate content in iron ore reverse flotation concentrate, a crucial indicator of plant performance. The proposed soft sensor model employs a dataset obtained from Kaggle, which includes measurements of iron and silicate content in the feed to the plant, reagent dosages, weight and pH of pulp, as well as the amount of air and froth levels in the flotation units. To reduce the dimensionality of the dataset, Principal Component Analysis, an unsupervised machine learning method, was applied. The soft sensor model was developed using three machine learning algorithms, namely, Ridge Regression, Multi-Layer Perceptron, and Random Forest. The Random Forest model, created with non-reduced data, demonstrated superior performance, with an R-squared value of 96.5% and a mean absolute error of 0.089. The results suggest that the proposed soft sensor model can accurately predict the silicate content in the iron ore flotation concentrate using machine learning algorithms. Moreover, the study highlights the importance of selecting appropriate algorithms for soft sensor developments in mineral processing plants.

2

Modified neural network based cascaded control for product composition of reactive distillation

Sakhre V., Jain S., Sapkal V. S., Agarwal D. P.

Polish Journal of Chemical Technology

|

2016

|

Vol. 18, nr 2

111--121

EN

In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.

3

Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements

Xu W., Fan Z., Cai M., Shi Y., Tong X., Sun J.

Metrology and Measurement Systems

|

2015

|

Vol. 22, nr 3

383--392

EN

This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.

4

Smart ore tracking system using soft sensor technology

Pan X.

AGH Journal of Mining and Geoengineering

|

2012

|

Vol. 36, no. 4

133-141

EN

The relationship between ore mineralogy and downstream processing is well known in the mining industry. In fact, the very definition of a mineral deposit as an ore body depends on its susceptibility to processing in an economical manner. With an ore tracking system, the information about each ore block, gained during exploration and mining, could be used as input data to the mineral processing operation. This would allow the real-time optimization and control of the ore processing. A smart ore tracking system can be developed by using soft sensor technique. The ore tracking system utilizes the real time information available in both SCADA and database of a mine. Using the ore geological data and the tonnage of ore being treated, the ore tracking system calculates and provides ore type information such as ore mixing percentage, ore grade and ore hardness and density. The ore type information provided by the ore tracking system can be made available at all process units at a mine, including primary crusher, primary stock pile, primary scrubbers, secondary scrubbers, secondary crushers, re-crusher stock pile, dense media separation stock piles, dense media separation feeders, and the feed to next processing plant. The smart ore tracking system, developed originally for a diamond mine, can be used for other mines, such as iron ore, coal, chromite ore, manganese ore, etc.

PL

Zależność pomiędzy mineralogią rudy a przeróbką jest bardzo dobrze znana w przemyśle górniczym. W rzeczywistości, definicja złoża mineralnego jako rudy zależy od jego podatności na procesy przeróbcze z punktu widzenia ekonomii. Przy zastosowaniu systemu "śledzenia" rudy informacja o każdym bloku rudy jest uzyskiwana podczas procesu wydobycia i obróbki górniczej. Może być ona zastosowana jako dane wejściowe do operacji przeróbki surowców mineralnych. Pozwoli to na optymalizację w czasie rzeczywistym i kontrolę procesów przeróbczych. Inteligentny system "śledzenia" rudy można uzyskać za pomocą techniki sensorowej. System ten korzysta z informacji w czasie rzeczywistym dostępnych zarówno w SCADA jak i bazie danych kopalni. Przy zastosowaniu danych geologicznych i informacji na temat ilości przerabianej rudy system oblicza i przewiduje informacje nt. rudy, tj. procent mieszania rudy, stopień rozdrobnienia, twardość, czy gęstość. Informacje te mogą stanowić bazę dla każdego procesu przeróbczego w kopalni, wliczając pierwsze kruszenie, pierwsze składowanie, pierwsze sortowanie, drugie kruszenie, drugie sortowanie, powtórne kruszenie, rozdział w cieczach ciężkich itp. System "śledzenia" rudy, wynaleziony pierwotnie dla kopalni diamentów może zostać zastosowany w innych kopalniach, tj. kopalnia żelaza, węgla, chromu, manganu itp.