Soft-sensors

Soft sensors Fault detection Data reconciliation Statistical analysis Parameter estimation... 

The previous section concentrated on the management of a hard and soft sensors network. This is an important step since the information sources must be carefully checked before being further used. This section will be devoted to the diagnosis of the overall biological state of the process. In particular, it will illustrate that the use of the Evidence theory approach improves the fault diagnosis system in terms of modularity and d3mamical adaptation. 

Inferential sensors, also known as soft sensors, are models that nse readily measurable variables to determine product properties critical to prediction of prodnct/process qnafity. Ideally the soft sensors are continuously monitored and controlled, or moiutored on a relevant time scale. They need to make predictions quickly enough to be used for feedback control to keep process variability to a minimum. 

Property prediction may be done nsing snch rontinely measnred parameters as temperatures, pressures and flow rates when there is snfficient process knowledge to correlate these values to product quality. Sometimes process analyzers snch as spectrometers are used to understand the process chemistry and kinetics, thus providing the ability to nse soft sensors if the tool that helped elucidate the critical process variables is unavailable. 

Due to the complexity of bioprocesses, and the lack of direct in-process measurements of critical process variables, much work is being done on development of soft sensors and model predictive control of such systems. Soft sensors have long been used to estimate biomass concentration in fed-batch cultivations. The soft sensors can be integrated into automated control structures to control the biomass growth in the fermentation. 

An example of the use of soft sensors is given by the automation of a penicillin production dependent on strict adherence to certain hmits in the fermentation process since such biological systems are sensitive to changes in operational conditions. An important issue in the use of soft sensors is what to do if one or more of the input variables are not available due, for example, to sensor failure or maintenance needs. Under such circumstances, one must rely on multivariate models to reconstruct or infer the missing sensor variable. ... 

M.J. Arauzo-Bravo, J.M. Cano-Izquierdo, E. Gomez-Sanchez, M.J. Lopez-Nieto, Y.A. Dimitraidis and J. Lopez-Coronado, Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems. Control Eng. Pract., 12, 1073-1090 (2004). 

Soft Sensors for Monitoring and Control of Industrial Processes Luigi Fortuna, Salvatore Graziani, Alessandro Rizzo and Maria G. Xibilia... 

Model Based Soft-Sensor for On-Line Determination of Substrate... 

Index Entries Soft-sensor substrate alcohol fermentation ethanol biomass. 

Application of adaptive neurofuzzy control using soft sensors to continuous distillation... 

Keywords distillation control, neurofuzzy networks, soft sensors, genetic algorithms... 

In this paper we describe the application of an adaptive network based fuzzy inference system (ANFIS) predictor to the estimation of the product compositions in a binary methanol-water continuous distillation column from available on-line temperature measurements. This soft sensor is then applied to train an ANFIS model so that a GA performs the searching for the optimal dual control law applied to the distillation column. The performance of the developed ANFIS estimator is further tested by observing the performance of the ANFIS based control system for both set point tracking and disturbance rejection cases. 

Application of Adaptive Neurofuzzy Control Using Soft Sensors to Continuous Distillation... 

S. Park and C. Han. A non-linear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns , Comp, and Chem. Eng. Vol. 24 (2000), pp. 871-877. 

Correlation-Based Just-In-Time Modeling for Soft-Sensor Design... 

Keywords soft-sensor, Just-In-Time modeling, recursive partial least squares regression, principal component analysis, estimation... 

In the present work, a new method for soft-sensor design is proposed. In the proposed method, referred to as correlation-based JIT (C-JIT) modeling, the samples used for local modeling are selected on the basis of the correlation instead of or together... 

In this section, conventional soft-sensor design methods are briefly explained. 

PLS has been widely used for building a soft-sensor because it can cope with a colinearity problem. Here X e and Y e are matrices whose ith rows are... 

The estimation performance of a statistical model will deteriorate when process characteristics change. Therefore, soft-sensors should be updated as process characteristics change. However, redesign of them is very laborious and it is difficult to determine when they should be updated. To cope with these problems, recursive PLS was proposed (Qin, 1998). Whenever both new input and output variables, and Pnew measured, the recursive PLS updates the model by using... 

Keywords Adaptive Control, Soft sensors. Ethanol, Fed batch process. 

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