Data-driven Soft Sensors in the process industry

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

The traditional soft sensor methods cannot be applied in the cases where uncertainty exists. To predict evaluation of variable with uncertainty, a novel method, called multi-evidential soft sensor model, was proposed based on evidence theory and multi-model strategy. In such method, the overall multi-model on the whole data space is established by weight sum of the multiple local models on local data spaces. For arbitrary given input vector x, each local model provides a prediction regarding the value of the output variable y, in the form of fuzzy belief assignment (FBA), defined as a collection of fuzzy sets of values with associated masses of belief. The output FBA is computed using a parametric, instance-based approach: imprecise training samples in the neighbourhood of x are considered as sources of partial information on the response variable y, and provide pieces of evidence reflecting the values taken by the response y; the pieces of evidences are pooled by using Dempster's rule of combination. To identify the parameters involved in the overall and local models, the so-called imprecise training samples are constructed from running data by means of data analysis and expertise knowledge.To validate the proposed method, a numerical experiment was designed based on a UCI dataset, and the experimental results suggest its power for predicting evaluation of variables with uncertainty. Finally, the prediction of unmeasured parameter level of coal powder filling in tubular ball mill was taken as the engineering example to validate the proposed method. The predicting results are in line with the experts' analysis. The two validations suggest that the proposed method is applicable for predicting variables with uncertainty in process industry.

Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty

Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima.SVM have been very successful in pattern recognition ,fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. This paper discusses least squares support vector machines (LS-SVM) estimation algorithm and introduces applications of the novel method for the nonlinear control systems. Then identification of MIMO models and soft-sensor modeling based on least squares support vector machines (LS-SVM) is proposed. The simulation results show that the proposed method provides a powerful tool for identification and soft-sensor modeling and has promising application in industrial process applications

Study on least squares support vector machines algorithm and its application

With the rapid development of ports, the port industry continues to focus on the function, service, and value chains. Further accelerating the high-quality development of ports has become crucial to promoting urban construction and economic development. Dongjiakou Port is an important shipping and logistics hub in the center of the Bohai Economic Rim and the Northeast Asia economic circle, which has superior deep-water resources. Through active transformation and upgrade, it has become an international trade hub with sustainable practices. This research examined Dongjiakou Port as a case study and employed a grey prediction model to analyze and predict port throughput and to determine the indicators and impact factors for a green port. In addition, the grey model and principal component analysis were used to predict the throughput of Dongjiakou Port from 2021 to 2025, and the effectiveness of the model was verified by comparing the predicted and actual throughput from 2014 to 2020. In addition, this research also provided suggestions for the sustainable development of ports from the aspects of port planning, port cargo structures, and effective port governance and development. The prediction model can provide support for future port planning and port design, to a certain extent, and can also provide references for predicting port throughput and promoting the construction of new ports and sustainable port development.

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Port Planning and Sustainable Development Based on Prediction Modelling of Port Throughput: A Case Study of the Deep-Water Dongjiakou Port

In this paper, SWOT analysis and value analysis are used to introduce the current operation situation of Petrobras and to study the development strategy and advantages and disadvantages of the company from different perspectives. Through analysis, we can find out whether the company's stock is worth investing at present.

/pdf/petrobras-corporate-strategy-and-advantages-for-future-2k7zebu6.pdf

Wen-hui Li

Papers

Study on least squares support vector machines algorithm and its application

Port Planning and Sustainable Development Based on Prediction Modelling of Port Throughput: A Case Study of the Deep-Water Dongjiakou Port

Petrobras' corporate strategy and advantages for future development