No abstract

Many industrial plants use regulatory control loops to achieve a stable operation of its processes and ensure that their products meet minimum quality parameters required by their customers. Moreover, the transformation processes of the raw materials into finished products can involve complex organization of equipment and energy use of different types (for example, electrical, chemical, mechanical, thermal, etc.). In this context, a major effort is undertaken by companies so that your plants are operated efficiently. But for that to happen it is essential that regulatory control is working properly. The evaluation of the regulatory control can be made periodically by means of indicators, so that corrective actions can be taken when significant performance degradation thereof occurs. Considering these issues, it was decided to conduct a pilot project to analyze the benefits that the regulatory control evaluation could bring to a company of the steel industry. The pilot project was conducted in a steam power plant belonging to the company. The approach of the methodology employed, the main items checked and the results achieved in this project are part of the scope of this text.1

-Reducing process variability is crucial to reach a more profitable operating point. Periodical disturbances, however, impose barriers to achieve this goal. Their effect can be strong since one disturbance that appears in a specific loop of a highly coupled plant can be seen in several loops. Thus, isolating their source and diagnosing their cause are essential. In this work, we describe the application of spectral independent component analysis to isolate a periodical disturbance that has a strong impact on the final variability in a polyethylene plant located in Southern Brazil. After the first analysis, the source was detected and the cause identified: valve stiction. To identify the cause (valve, bad tuning, or periodic disturbance), we used the methodology based on higher-order statistics. Once the valve problem had been overcome, the product variance was reduced by 93%.