Advanced Process Control

Operators of energy systems are continuously facing higher market competition and stricter environmental regulations. These are the issues that challenge advances of engineering systems in terms of system operation, system efficiency, pollution and technical limitations. Moreover, due to rapid development of new technologies, the complexity of systems and related number of limitations are increasing, wherein the primary controls of systems can no longer tackle the problems appropriately.

In this regard, JS energy Team offers solutions of Advanced Process Control (APC). It is an answer to contemporary complex problems of the power and CHP industry. The APC comprehends multi-input multi-output interactions where system constraints and goals, such as economical factors, system efficiency and system limitations, are considered in real-time control. The core of the APC is based on Model Predictive Control (MPC) technology.

Principle of the MPC

MPC is used in a majority of existing multivariable control applications and is the technology of choice for many new advanced multivariable control applications. Its success rides on the increase of computing power with many important practical advantages. The MPC algorithm for the calculation of an optimal strategy of input process variables uses:

  • Mathematical model of a system
  • Historical process data
  • Cost function over a controlled horizon

A general formula of a cost function can be expressed with the following equation:

The following figure schematically represents an idea of the MPC control technology.


Schematic representation of the Model Predictive Control Technology

MPC control is based on iterative optimization procedure over a predicted horizon Hp, the result of which are the optimal settings of manipulated variables. At present time t the current state of the system is applied to calculate optimal strategy based on cost function for the future interval [t, t+Hc]. It is an online calculation of optimal settings based on current and past measurements of controlled variables, desired goals and system limitations.

Advantages

APC represents state-of-the-art approach in the control of complex systems with multiple and interrelated process parameters. Advantages of the APC are:

  • Explicit use of a process model: by applying a process model and past measurements, a future system’s activity can be simulated. The simulations can hence be used to determine optimal settings of manipulated variables
  • Explicite handling of system constraints: the optimization procedure considers systems physical constraints providing a solution that does not violate the systems capabilities
  • Straightforward formulation based on well understood concepts
  • Well understood tuning parameters (weights, prediction horizon, optimization problem setup, etc.)
  • Tackling of complex systems with multiple inputs and multiple outputs
  • Easy application of static optimization in addition to the MPC’s dynamic optimization procedure
  • Easy to maintain: changing a model or altering the specifications does not require a complete redesign, most of the times can be done on the fly
  • Development time is much shorter than for the competing advanced control methods

Benefits

The application of APC solution yields substantial benefits:

  • Increased system’s efficiency due to improved productivity, increased throughput and reduced energy consumption
  • Improved system’s reliability, availability and reduced down time
  • Increased safety due to operation within process constraints
  • Extended asset life cycle
  • Decreased operating costs
  • Increased operating flexibility

Application Areas

Our services include the following areas of the APC applications:

  • Steam cycles (steam pressure control, optimization of industrial boilers, etc.)
  • Combined cycles
  • Pharmaceutical industry
  • Petrochemical industry
  • Chemical industry
  • Pulp and Paper Industry



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