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Motivation of economic performance assessment
Control systems are widely applied to satisfy safety requirements and environmental regulations and to improve productivity. Maximising profits is, however, often regarded as the primary objective (Seborg et al., 2004). When considering the implementation of a control upgrade project, several measures must be considered: control performance, process performance and economic performance (Friedmann, 2006).
Control performance is a measure of how well the control system achieves its objective. This objective is usually expressed in terms of product quality and performance can be measured statistically and expressed as some function of deviation from target or variability. Among the commonly used functions are range, maximum error and standard error.
Process performance is a measure of how well the process achieves its objective. Various criteria can be used as measures. Most processes use energy to convert raw material into a product. Measures of process performance include production rate, expressed as units of product per unit time, and efficiency, expressed as units of product per unit of raw material and/or energy.
Improvement of control performance does not necessarily improve process performance. Sometimes it does, but often it only creates a platform for improved process performance. More often, process conditions or operating procedures have to be changed to take advantage of better control.
Economic performance is measured in financial terms. Industrial processes are usually operated to make money. Economic performance, like process performance, can be stated in terms of production or efficiency. The relationship between process performance and economic performance is similar to that between control performance and process performance. Improved process performance does not necessarily guarantee that the process will make more money. Operating practices must often be changed to realise the potential benefits (Friedmann, 2006).
1 Introduction
1.1 Motivation of economic performance assessment
1.2 A brief history of economic performance assessment
1.3 Overview of the current literature
1.3.1 Origins of benefits from control systems and problems involved in economic
performance assessment
1.3.2 ‘Warren family’
1.3.3 ‘Integral family’
1.3.4 Other methods
1.3.5 A framework of economic performance assessment
1.4 Research objectives and approaches
1.4.1 Research objectives
1.4.2 Research approaches
1.5 Contributions
1.6 Organisation of thesis
2 Preliminary economic analysis
2.1 Process operation and control understanding
2.2 Identification of possible benefit
2.2.1 Higher throughput
2.2.2 Lower utility costs .
2.2.3 Better yield
2.2.4 Fewer unwanted byproducts
2.2.5 Less labour
2.2.6 Better quality
2.2.7 Quantifiable benefits from reducing pollution
2.2.8 Quantifiable benefits from improving safety
2.3 Conclusion .
3 Review of a framework on economic performance assessment
3.1 Introduction
3.2 Step 1: Base case identification
3.2.1 Collect base case data of controlled variables of interest
3.2.2 Estimate the base case’s probability density functions
3.2.3 Develop performance functions ((y))
3.2.4 Calculate the economic performance index of the base case ( base)
3.2.5 Estimate the minimum variance (2 mv)
3.2.6 Determine the operating point (μmv) of CVs for 2 mv
3.2.7 Calculate the maximum achievable performance ( mv)
3.3 Step 2: Advanced controller design
3.4 Step 3: Estimation of performance and initial economic assessment
3.4.1 Estimate the standard deviation improvement (esti − base) due to the advanced controller
3.4.2 Determine the optimal operating point (μesti) for the advanced controller
3.4.3 Estimate the economic performance index with the advanced controlle ( esti)
3.4.4 Do fixed cost estimation
3.4.5 Conduct initial economic project evaluation and decision making
3.5 Step 4: Implementation of the advanced controller
3.6 Step 5: Calculation of performance and success assessment
3.6.1 Collect data of controlled variables of interest .
3.6.2 Determine the probability density functions of controlled variables .
3.6.3 Calculate the economic performance index of the advanced controller ( adv)
3.6.4 Calculate the achieved profit ( adv − base)
3.6.5 Derive the new optimal operating point (μadv2) based on the standard deviation obtained in Step 2.3 (adv)
3.6.6 Update the minimum variance (2 mv2) estimate and maximum achievable
performance ( mv2)
3.6.7 Do statistical experiments to examine economic improvement
3.6.8 Conduct final economic project evaluation
3.7 Conclusion
4 Performance functions and a multivariate economic assessment of a furnace
4.1 Introduction
4.2 Process background and economic mode
4.3 Review of the development of individual performance functions of the furnace .
4.4 Development of joint performance functions of the furnacebles
4.5 Methodology for the economic assessment of the furnace controllers .
4.6 Economic assessment of a model predictive controlled furnace
4.7 Conclusion
5 State-of-art of control and performance assessment of grinding mill circuits
6 Performance function development: Grinding mill circuits
7 Economic performance assessment of three milling circuit controllers
8 Investigation of the performance functions for the final stage of a bleach
process
9 Methodology for developing performance functions
10 Conclusions
