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    STATISTICAL PROCESS CONTROL OF MULTIVARIATE WASTEWATER PROCESSES

STATISTICAL PROCESS CONTROL OF MULTIVARIATE WASTEWATER PROCESSES

In now days, Statistical Process Control (SPC) concepts and methods are very important in the manufacturing and process industries. Their objective is to monitor the performance of a process over time in order to verify that the process is remaining in a "state of statistical control". Process operation monitoring is carried out to ensure that the process operation complies with requirements on product quality, process safety and efficient use of resources. In most industrial applications, including wastewater treatment (WWT), process performance and operation are measured continuously. Often, the number of measured variables is high, demanding a structured approach to monitoring and analysis of the process. Multivariate statistics(MVS) provides a methodology to extract and structure information from large amounts of data.

MVS has been used to monitor industrial processes for several decades.  It has also been applied to WWT operation. There are some ways to apply the multivariate process. Straightforward ways are to identify new monitoring models as the process conditions change or to use a number of models and choose among them using a suitable criterion. A somewhat more sophisticated way is to constantly and automatically update the model make the model adaptive. This can be achieved by using a moving window including historical data or by calculating the model recursively.

Multivariate statistical modeling strategy can be widely applied to complex and time- varying biological systems to develop cost-effective and reliable process models. Were interested in continuing to apply this modeling techniques to various environmental systems.


Data-driven Soft Sensor Approach For Quality Prediction in a Environmental Industrial Process

In recently, industry has been characterized as a highly complex process and equipped with modern control systems. Advanced process control (APC) is widely used by control engineers to optimize the process operation in order to produce a product with specified quality. The success of the on-spec quality production is based on the model between the quality and process variables. It is difficult to develop their relationship using the first principle due to the complexity of the process. Many of the environmental processes, the quality is measured in analytical laboratory at irregular intervals and it involves resources and considerable time delay. Reliable on-line prediction of quality is extremely beneficial in this environment.

A soft sensor is an inferential model based on software technique to determine the value of a process variable. This is in contrast to physical sensor that directly measures the value of the process variable. Some kinds of the case of complex system where the process mechanism is not well-understood, empirical models developed by system identification techniques are used to derive the implication among the variables. Among the techniques, neural network technology and multiple linear regressions have been employed as a data-driven approach to modeling problem.

Neural Networks are powerful tool for nonlinear system identification and they have variety applications such as process control, signal processing, pattern recognition, process monitoring, finance and chemistry. Many advantages can be identified from their applications to inferential modeling. They can easily grasp the nonlinear relationship among the variables and approximate it to the desired degree. Within the structure of neural nets, a model can be obtained with little prior detailed knowledge of process behavior. However, their successful use and applications have to be driven by expertise and a well sounded development procedures in order to avoid failures and disappointment. Multiple linear regressions can also be used for inferential model development for their straightforwardness and simpleness of relationship explanation.

 

However, they suffer from the numerical problems when a strong correlation exists among process variables, and they may degrade the model with the collinearity. Partial least squares method has established itself as a robust alternative to the ordinary least squares technique in the analysis of correlated data. It will provide a transparent model and a unique solution, in contrast to the "black box" and non-uniqueness nature of Neural Nets.

We use soft-sensor techniques that already explained to overcome the problems with environmental processes. The applications will be taken to approach various environmental processes and that constructed soft-sensors lead optimum condition to make the best products.

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