Cookies help us deliver our services. By using our services, you agree to our use of cookies. Learn more

Improve your process by Data Reconciliation and Validation

ChemPlant Technology offers In-house Courses on RECONCILIATION targeted at better use of Plant Data

What is Data Reconciliation and Validation (DRV)?

A method of data processing using all information present in plant data. DRV nowadays becomes a standard method for data used in balancing, mass and energy accounting, on-line modeling. Plant performance monitoring and analysis and advanced process control.

Course overview


  • Mathematical models
  • Statistics
  • Measurement errors


Usually more data is measured than necessary. Reconciliation makes redundant data consistent with the mathematical model. Moreover, unmeasured parameters of the model are estimated on the maximum likelihood principle. Reconciled data are generally more accurate than the measured ones. In general, reconciliation is a method for optimum estimation of model's parameters. Moreover, reconciliation represents basis for other activities related to validation of plant data - especially for elimination of gross measuring errors and to optimisation of the overall measurement process.

Propagation of measurement errors

Results of data processing are usually of different accuracy. In practice we can meet in some cases with errors in hundreds of per cents of real values. Information about accuracy of results (confidence intervals) should accompany all values important in further decisions.

Data analysis

Confront your data with the model. A bad fit can be caused either by gross measurement error, or by a model error. Both discrepancies can devalue your results.

Detection and identification of gross measurement errors

Reconciliation provides powerful tools for detection of gross errors presence and also for finding sources of gross errors (gross errors identification).

Model errors

Sometimes are discrepancies between data and model caused by model's inadequacy (neglecting process dynamics, unmeasured leaks, too simplified models of unit operations, etc.). Reconciliation represents an efficient method of model building, to incorporate more complex features in the model.

Measurement optimisation

Even advanced methods of data processing can't substitute for a bad measurement plan. Reconciliation provides methods for either optimising existing instrumentation system, or designing a new one.

Process data management

Historical data present in process data historians contains the invaluable information which can be used for efficient plant performance monitoring and analysis. The DRV Course also deals with management of big data sets where data should be transformed into an information needed for process performance monitoring and optimization.

Case studies

The most important fields of reconciliation will be covered: mass, component and energy balancing reconciliation based on linear and non-linear models

  • Models of chemical plants
  • heat exchangers (identification of heat fluxes and heat transfer coefficients)
  • steam and gas turbines
  • industrial boilers
  • classical and nuclear power plants
  • mass and energy accounting in a large plant (reconciliation as a part of plant information system)
  • optimal design of measurement placement

Course features

  • computer workshops with professional reconciliation software
  • possibility to solve user-specific problems with a possibility to tailor the course according to user needs
  • every participant will acquire a software needed for solving basic reconciliation tasks
  • course text is an unique reconciliation minibook comprising the most important techniques of reconciliation

Course duration

Depends on course's target. Usually 2 or 3 days is satisfactory for managing basics of reconciliation technology.

Who should attend?

  • Yield Accounting managers
  • managers and process engineers of operating plants
  • staff of optimisation, retrofitting and debottlenecking groups
  • control engineers, especially dealing with advanced control.

Previous courses:

Beaminster (1991, 1992, 1993, 1994, 1995, 1997, 1998) Great Britain
Buenos Aires (1999, 2000, 2004) Argentina
Hanau (2015) Germany
Cumana (2006) Venezuela
Heath (1996) Great Britain
Maracaibo (2007) Venezuela
Neuquen (2005) Argentina
New Brunswick (1997) USA

Paris (1990)

Philadelphia (1991) USA
Stockholm (1993, 1994, 1995) Sweden
Veszprem (1993) Hungary
Wilton (1996) Great Britain
Isla Margarita (2012) Venezuela
San Lorenzo (2012) Argentina

About the Lecturer

Dr. Frantisek Madron has been involved in advanced process data analysis for many years (his first paper on reconciliation was published in AIChE J. in 1977). Since then he is an author or co-author of more than 50 papers and three books on process plant analysis. The Process Plant Performance (measurement and data processing for optimization and retrofits), published in 1992 by E.Horwood, represents the comprehensive coverage of plant data processing. His second book, Material and Energy Balancing in the Process Industries (co-authored with V.V.Veverka) is the exhaustive text covering the subject from microscopic balances to yield accounting of whole companies. The booklet Process Optimization in Power Industry: Data Validation (co-authored with V. V. Veverka and M. Hostalek) is targeted at DR in power plants.

brand_name - txt:poweredby