Advanced Process Automation

Discusses the techniques available for processing the data within process control and related systems for use in plant and enterprise management. Topics covered are - computer integrated manufacturing (CIM); manufacturing business organisation, MRP, open systems, client-server, management execution systems, OPC: real-time information systems: relational databases; structured query language SQL, entity-relationship modelling, integrated spreadsheets, data & decision support hierarchies: statistical process control (SPC); data variance, multivariate statistics, principal component analysis, condition monitoring, control charts: introduction to optimisation; costs and benefits, constraint and equality models, linear programming: introduction to fuzzy logic, neural nets & expert systems.

Practical work consists of exercises designed to provide experience in the use of the query language SQL.

Aims
To provide an understanding of the statistical techniques and database technologies that enable management information to be abstracted from historic and real-time data for decision support. 

Objectives
To develop a deep understanding of the application of statistical techniques to process control. 
To study relational databases and the concepts upon which they are based. 

To become familiar with the principles of computer integrated manufacturing (CIM) systems and production management. 

To introduce the use of real-time databases for decision support. 

To provide an introduction to linear programming as a basis for the CME 8390 module on Optimisation and Scheduling. 

To provide an introduction to some of the technologies involved in the CME 8386 module on Fuzzy, Neural and Expert Systems. 

Phasing
It is essential that delegates have completed (or be familiar with the material covered in) the Mathematics and Matlab (CME 8360) module before doing this one. 

It is desirable, but not essential, that delegates have completed (or have some familiarity with the material covered in) the Batch Control and Application Software (CME 8372) and the Control System Technology (CME 8378) modules before doing this one. 

Study Modes
This module is of one week's full-time intensive study consisting of a variety of lectures, informal tutorials for problem solving and structured computer-based laboratory work.  It is followed by an assignment to be carried out in the delegate's own time. 

Coursework
The time allocation for practical work provides for hands-on experience of querying databases using SQL. 

Recommended Texts
Edgar T F, Himmelblau D M & Lasdon L S,  Optimisation of Chemical Processes,  2nd Edition,  McGraw Hill,  2001 

Elmasri R & Navathe S B,  Fundamentals of Database Systems,  Addison Wesley, 2003. 

Hicks J O,  Management Information Systems: A User Perspective,  3rd Edition,  pub West,  1993. 
Love J,  Process Automation Handbook, Springer, 2007. 

Warwick K, Irwin G and Hunt K,  Neural Networks for Control and Systems,  IEE (Peter Peregrinus Ltd),  1992. 

Topics Included
Optimisation for planning and management:  Introduction to linear programming (LP).  LP models and the simplex method.  Introduction to non-linear programming (NLP).  Types of NLP problems. 

Computer integrated manufacturing (CIM):  Overview of benefits and decision making, levels and types.  Distinction between CIM, management information systems (MIS) and flexible manufacturing systems (FMS).  Manufacturing business organisation.  Functionality of typical packages for planning, sales & marketing, business & admin services, etc.  Materials resource planning (MRP), MRP2 and process MRP.  Scheduling and production planning, eg lot sizing.  Hybrid processes.  Traceability.  System structures.  Open systems, relational databases (RDB) and client-server technology.  Management execution systems (MES).  Object linking and embedding (OLE) and current developments in OLE for process control (OPC). 

Databases:  Introduction.  Concept of relational databases (RDB).  Differences between conventional, relational and object oriented databases.  Structured query languages (SQL).  How to interrogate databases.  Dependency theory and how data items relate to each other.  Normal forms of RDB.  Database design.  Entity-relationship modelling and deriving databases.  Real-time relational databases.  Performance calculations.  Integrated spreadsheets.  Hierarchies of data and decision support. 

Statistical process control (SPC):  Review of stochastics: summary statistics, covariance, correlation coefficients, probability distributions, etc.  Linear regression analysis: pre-processing of data, least squares, model validation, goodness of fit, multiple linear regression (MLR), cause and effect relationships.  Principal components analysis (PCA): underlying concepts, bivariate and multivariate analysis, reduction of dimensionality, interpretation of components.  Projection to latent structures (PLS), non-linear PLS.  Condition monitoring: data collection, prescreening, validation and reconciliation.  Control charts: average, moving average and spread.  Limits: action and warning lines.  Multivariate SPC: principal components plots, Hotellings limits. 

Introduction to fuzzy-logic control, artificial neural nets and expert systems.  Real-time applications.