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Master’s in Industrial and Economic MPC Engineering

Online master's programme of 1,900 hours in industrial and economic MPC engineering, structured in 12 modules covering identification for MPC, linear, economic, nonlinear, robust and stochastic MPC, real-time optimisation and industrial APC implementation and maintenance, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

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Learning outcomes

What you will be able to do

01

Identify dynamic models of multivariable processes.

02

Formulate constrained predictive controllers.

03

Integrate economic optimisation and control layers.

04

Design economic and nonlinear MPC controllers.

05

Apply robust and stochastic MPC.

06

Implement advanced controllers in DCS systems.

07

Maintain and audit the performance of advanced control.

Profile

Who it's for

  • Advanced process control engineers.
  • Real-time optimisation specialists.
  • Chemical process engineers looking to move into APC.
  • APC consultants who want a stronger theoretical base.
  • Energy engineers managing plants with variable prices and demand.
  • Researchers in predictive control.
  • Engineering or science graduates seeking to enter refining and petrochemicals.
Standards & tools

What you'll work with

  • IEC 61131-3:2013 (programming languages for programmable controllers)
  • ISA-95 (IEC 62264-1:2013) (enterprise-control system integration)
  • ISA-88 (IEC 61512-1) (batch control models and terminology)
  • IEC 61511-1:2016 (safety instrumented systems for the process industry)
  • ISA-18.2-2016 (management of alarm systems for the process industries)
  • NAMUR NE 107 (self-monitoring and diagnosis of field devices)
  • MATLAB Model Predictive Control Toolbox
  • CasADi
  • acados
  • do-mpc
  • Aspen DMC3
Syllabus

12 modules · 1900 h

  1. 1.1Control hierarchy in the plant
  2. 1.2Regulatory control with PID
  3. 1.3Limitations of classical control
  4. 1.4History of industrial predictive control
  5. 1.5Economic benefits of advanced control
  6. 1.6Identifying APC opportunities
  7. 1.7Preliminary benefit studies
  8. 1.8Manipulated variables, controlled variables and disturbances
  9. 1.9Refining and petrochemical cases
  10. 1.10Case study: APC benefit study
  1. 2.1Step and PRBS tests
  2. 2.2Multivariable tests and GBN
  3. 2.3FIR and step response models
  4. 2.4State-space models
  5. 2.5Subspace identification
  6. 2.6Validation and uncertainty analysis
  7. 2.7Gains and collinearity
  8. 2.8Closed-loop identification
  9. 2.9Commercial identification tools
  10. 2.10Lab: identification of a distillation column
  1. 3.1Prediction model
  2. 3.2Cost function and weightings
  3. 3.3Hard and soft constraints
  4. 3.4Solution by quadratic programming
  5. 3.5DMC and GPC
  6. 3.6Disturbance estimation
  7. 3.7Zero offset and integral action
  8. 3.8Stability and infinite horizon
  9. 3.9Tuning parameter adjustment
  10. 3.10Practical: constrained linear MPC in Python
  1. 4.1Steady-state optimiser within the MPC
  2. 4.2Linear programming of targets
  3. 4.3Constraint priorities
  4. 4.4Economic optimisation of setpoints
  5. 4.5RTO with rigorous models
  6. 4.6Integration between RTO and MPC
  7. 4.7Management of degrees of freedom
  8. 4.8Steady-state robustness
  9. 4.9Example in furnaces and boilers
  10. 4.10Workshop: LP layer of a multivariable controller
  1. 5.1Direct economic cost formulation
  2. 5.2Stability in economic MPC
  3. 5.3Dissipativity and optimal steady state
  4. 5.4Periodic and transient operation
  5. 5.5Variable energy prices
  6. 5.6Real-time demand management
  7. 5.7Optimal production versus quality
  8. 5.8Comparison with tracking MPC
  9. 5.9Practical implementation of economic MPC
  10. 5.10Practical: economic MPC of a heat pump
  1. 6.1First-principles nonlinear models
  2. 6.2Nonlinear optimisation (NLP)
  3. 6.3Orthogonal collocation and multiple shooting
  4. 6.4IPOPT and acados solvers
  5. 6.5Real-time iteration
  6. 6.6Moving horizon estimation
  7. 6.7Hybrid and Hammerstein models
  8. 6.8Neural networks as models
  9. 6.9Computational cost and real time
  10. 6.10Lab: NMPC of a CSTR reactor
  1. 7.1Model uncertainty
  2. 7.2Tube-based MPC
  3. 7.3Min-max MPC formulation
  4. 7.4Stochastic MPC with chance constraints
  5. 7.5Scenarios and trees
  6. 7.6Safety constraints
  7. 7.7Robustness validation
  8. 7.8Conservative tuning under uncertainty
  9. 7.9Applications in energy
  10. 7.10Practical: MPC with chance constraints
  1. 8.1Distillation columns
  2. 8.2FCC cracking units
  3. 8.3Furnaces and boilers
  4. 8.4Exothermic chemical reactors
  5. 8.5Cement plants
  6. 8.6Pulp and paper
  7. 8.7Water treatment
  8. 8.8Building HVAC
  9. 8.9District heating and cooling networks
  10. 8.10Case study: MPC of a clinker kiln
  1. 9.1Commercial APC platforms
  2. 9.2Integration with the DCS
  3. 9.3OPC communication architecture
  4. 9.4Simulator testing
  5. 9.5Phased commissioning
  6. 9.6Operator training
  7. 9.7Management of modes and disconnections
  8. 9.8Controller documentation
  9. 9.9Management of change
  10. 9.10Workshop: APC commissioning plan
  1. 10.1MPC performance monitoring
  2. 10.2Performance indices
  3. 10.3Model degradation
  4. 10.4Adaptive model re-identification
  5. 10.5Diagnosis of regulatory loops
  6. 10.6Service factors
  7. 10.7Periodic controller audits
  8. 10.8Quantification of benefits
  9. 10.9Sustainability of APC
  10. 10.10Practical: audit of a multivariable controller
  1. 11.1Distributed and cooperative MPC
  2. 11.2Hierarchical MPC in plants
  3. 11.3Data-driven control
  4. 11.4Machine learning and MPC
  5. 11.5Digital twins for control
  6. 11.6MPC in embedded systems
  7. 11.7Microgrid optimisation
  8. 11.8Decarbonisation and control
  9. 11.9Current research lines
  10. 11.10Case study: distributed MPC of a microgrid
  1. 12.1Selection of the process and objectives
  2. 12.2Model identification
  3. 12.3Design of the MPC controller
  4. 12.4Economic optimisation layer
  5. 12.5Simulation and tuning
  6. 12.6Robustness analysis
  7. 12.7Benefit evaluation
  8. 12.8Conclusions and implementation proposal
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB Model Predictive Control Toolbox, CasADi, acados, do-mpc and Aspen DMC3, in free, academic or evaluation versions. Tests are studied on real procedures: identification of process models, tuning of multivariable controllers in simulation and calculation of the economic benefits of APC. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEC 61131-3:2013, ISA-95 (IEC 62264-1:2013), ISA-88 (IEC 61512-1), IEC 61511-1:2016, ISA-18.2-2016, NAMUR NE 107.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEC 61131-3:2013, ISA-95 (IEC 62264-1:2013) and ISA-88 (IEC 61512-1) applied to real cases, not as a theoretical annex.
Industry software:MATLAB Model Predictive Control Toolbox, CasADi, acados, do-mpc and Aspen DMC3, in free, academic or evaluation versions.
Master's Thesis with defence:an MPC controller with an economic optimisation layer for a real process, from model identification to robustness and benefit evaluation, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Process model identification
Identify a multivariable process model from step and PRBS tests.
MPC controller design
Formulate and tune a constrained MPC controller in simulation.
Economic optimisation layer
Add an LP or RTO layer that sets economically optimal targets.
Robustness and benefit analysis
Analyse controller robustness and quantify the economic benefits of APC.
Master's Thesis
A complete industrial MPC solution with an implementation proposal, defended before a panel.
Career opportunities

Where this takes you

Advanced process control engineerReal-time optimisation specialistChemical process engineerAPC consultantEnergy engineerPredictive control researcher
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB Model Predictive Control Toolbox, CasADi, acados, do-mpc and Aspen DMC3, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture, or equivalent professional experience, plus English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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