Paul Van Buskirk
Quality Monitoring & Control

Abstract

Advances in AI modeling techniques, such as artificial neural networks, e-Model, genetic algorithms, VB-Model, etc., provide a robust tool for monitoring, control and optimization of a system. Monitoring is used for data verification, fault detection, forecasting and/or prediction. Total system or subsystem performance can be ranked when combined with statistical methods. When variables are adjustable, the system can be regulated to provide the "Very-Best" results through direct or fuzzy-logic control and optimization methods. This is not for the future; this is the capability that personal computers and artificial intelligence provide today.

Introduction

Model development is an investment. Recent advances in PC and AI systems provide a significant reduction in this investment. And, there are numerous model applications that will produce a return-of-investment. This article gives an overview of model applications, available now, to improve the profitability, quality, safety or environmental controls of a process. Extensions of these applications can be made to any industry that has data.

This article highlights neural networks as a model development tool, due to its widespread use and success. Other AI modeling methods can be used. The model developer should determine the best technique for a given application.

Background

The advantage of AI modeling techniques, such as artificial neural networks (ANN), are that a considerable amount of time can be saved in model generation when data is available. Development of an equation based (fundamental) model for multivariable non-linear systems can be virtually impossible with certain processes. Furthermore, a fundamental model may require hundreds if not hundreds of thousands of equations. The number of calculations and resulting CPU times can exceed the actual system response in real time. Conversely, ANN models are computationally efficient. The code required typically does not exceed three pages, even for large systems.

ANN model development is data intensive. A database, or spreadsheet, contains the data that the ANN model uses. A column in the spreadsheet represents a single variable. A column can be used as an independent "input" or as a dependent "output" variable. With neural nets, multiple outputs are allowed. How the output variable(s) change with changes in the input variables is what the neural net "learns". A row of data across all used variables is an event the neural net will use to learn the cause-and-effect dependence. The ANN model then "trains" until the error between the predicted and actual value, across selected rows of data, is reduced to a satisfactory level. An introduction into how neural-networks work is provided in reference (1).

ANN model generation does not come without risk. The ANN model developer needs to follow certain data screening guidelines to ensure integrity in the data, please see the General recommendations for the AI model developer. The work involved with data screening can occupy the bulk of the time in ANN (or any other) model development. The ANN model software provider will also supply guidelines for software use, data screening, and model development. Numerous technical publications address issues related to ANN model data preparation, PC AI is an excellent resource for these articles.

When these guidelines are followed, an accurate and robust ANN model can be developed that can be used for multiple and strategic purposes. A general overview of these model applications is provided in AI model applications and utilization.

The applications of a system model are not limited to the items listed. Endless possibilities exist. End use will be as varied as the personnel and industries that develop or utilize the model. The applications listed are available now, through numerous software providers. Implementation will provide a technology advantage, with benefits that improve profit, quality, environmental and safety controls.

As shown above, applications of an ANN model can range from sensor & data verification to forecasting to full process optimization. In most cases the required ANN model(s) for these applications can be developed from the same database. For a given process or system, these applications should be used together.

This approach provides constant monitoring of the sensors, equipment, controls, and total system performance. Deterioration in the performance of any component in a system can then be quickly pinpointed and corrected. As a result, the stability of the process will be improved, providing an increased utilization of the control and optimization applications. Maximum utilization of the control and optimization applications will increase the process profit and quality. Additionally, safety and environmental goals will be maintained and improved.

The following provides a brief discussion of selected items as listed in the AI Model Applications and Utilization:

Utilization of ANN models for sensor validation and fault detection is in common use. A reliable fault detection technique that uses several AI modeling tools is described in reference (2). This application is most effective when all sensors are monitored as part of a preventative maintenance program. The keys to successful process control and optimization implementation are reliable and accurate sensors. 

Sensor Error Meter provides an example of a sensor validation and error detection application.

Prediction is primarily used for forecasting. Applications include market analysis, sales projections, product properties, process variable inferred values, etc. Reference (3) is a good starting point for the utilization of ANN models in the process industries. There are also several articles in past issues of PC AI Magazine. The major use of ANN models in the process industries is in the prediction mode, either real-time or for off-line analysis and troubleshooting.

Sensitivity analysis is used to determine the important variables in a process. This provides a fundamental understanding of the process in terms of key variable rankings to control a process result. Numerical derivatives (D Output/ D Input) of the ANN model provides the sensitivity results. Most ANN software providers furnish this feature. Sensitivity analysis is classically the first step towards implementing supervisory controls into a process or system. Numerical derivatives of a process provide what is termed the gain array, which is the starting point for multivariable control and optimization.

Equipment performance monitoring can be applied through the use of several approaches. A direct approach is to develop an AI model to determine the performance index of the equipment. The calculated index from the AI model is then monitored to determine the performance decay rate. From the decay rate preventive maintenance requirements are determined using "rule-based" AI technologies.  The Equipment Performance Meter shows a typical screen. This type of approach can be used for the majority or process equipment designs and subsystems.

Equipment Performance Meter

Total process indexing provides a single auditing measurement to monitor the performance of a process or system. This method combines the sensitivity of a process with statistical process control (SPC). The auditing measure, "Process Index", is calculated by the following equation (4):

Where xi = data point evaluated at ith sampling of input variable x.

Fi = ANN model prediction at ith sampling point.

= Target input variable setting.

s s = Standard deviation.

= 2.71828182846.