Soluciones avanzadas de control de procesos

Advanced process solutions

ECN Automation has developed process specialties that allow us to meet defined standards for some industries, create solutions for complex systems or pre-defined package solutions for specific processes.



Batch Control

The batch processing based on the S88 standard defines a structure independent of the control system (DCS, PLC, SCADA or HMI), it is independent of the equipment supplier or brand, and can be applied to automatic, semi-automatic and / or manual operations.

At ECN Automation with more than 30 years of experience we have developed capabilities to carry out S88-based projects to meet the highest requirements of the food, beverage, life sciences, pharmaceutical and chemical industries.
This standard separates the process into two models: equipment (physical) and procedures.

The physical model includes the following elements: company, site, area, process, unit, equipment module, and control module.
The procedural model contains the strategy or steps to follow to carry out the Production Lot. At each level of the physical model there corresponds an action of the procedure model (Diagram 3).

Characteristics

  • Create and manage recipes, and run them automatically
  • Reduce validation and commissioning time
  • Configure physical and procedural models
  • Collect electronic batch data and reports
  • Integrate batch and recipe data with corporate information systems
  • Simulate the batch process
  • Preserve and lock recipe versions
  • Map and track approval flows
  • Help prevent unauthorized access

Benefits

  • Reduce validation and commissioning time
  • Help prevent unauthorized access
  • Elimination of ambiguity
  • Common terminology
  • Reuse
  • Clarity of design
  • Validation

The batch system has very noticeable qualities since they are normally used where the processes have different types of stages and some in a repetitive way, that is, recipes.

The ISA / ANSI S88 structure allows to clearly identify process lines, equipment, operations, actions, etc. It also makes it possible to identify the raw materials used, their transformation and the products generated. This helps the user to generate product traceability and genealogy reports.

Batch process scheduling examples

Case studies

01

Quimikao

Scope:

Batch-based control of their hydrogen plant.

02

Condumex

Scope:

Modernization of the electrical cable insulation manufacturing plant

03

Tequila Herradura

Scope:

Instrumentation and process control system for their hydrolysis plant.



Advanced process control (APC)

Currently, plant processes demand efficiency and complexity due to the non-linear characteristics of the process and the high interaction between input and output variables (MIMO). For this reason, the application of advanced control is the solution that is needed today, taking advantage of the technological advances in hardware and software that facilitate the development of algorithms or control strategies different from the classical linear control.

Objectives of the APC:

The main objective is to reduce the variability of the process until stabilizing it and in a second stage optimizing it (maximizing) to obtain these results:

  • Improve productivity
  • Reduce energy consumption
  • Improve product quality and consistency

The business cases of an APC project support that the return on investment will come mainly from the variability of the process and the impact that this will have on the economic KPIs of the plant, which are estimated quantitatively by doing a plant profile study in which 3 to 6 months of operation are analyzed to estimate losses

The return on investment of an implementation of an APC system is obtained from the reduction of the variability and the increase of the production or performance of the process.

The Advanced Control is based on a feedback control loop but improved through an optimization algorithm:

Benefits

By having advanced control for your equipment or processes, you optimize your operation, which is reflected in the following:

  • Increase in production
  • Reduction of downtime
  • Decrease in maintenance on the equipment, since the control gives us a prediction of the operation, where it can tell us when to do small maintenance so as not to have the need to do a major maintenance or a replacement of a damaged equipment
  • Cost reduction, since, instead of doing three maintenances a year, you can reduce them to one or that the time of these maintenances is longer

Boiler control

Mill control

Reactor contro

Control of distillation towers



Fuzzy Logic

The incorporation of fuzzy logic into control systems gives rise to what is known as fuzzy control systems. Within the control systems there are two large areas, modeling or identification and control itself or direct control.

The idea is very simple, it is about determining in a logical way what should be done to achieve the control objectives in the best possible way from a knowledge base provided by an operator, without this base it is not possible to develop an application and that this works correctly.

The knowledge and experience of an operator is used to build a controller that emulates its behavior. Compared with traditional control, fuzzy control has two practical advantages, one is that the mathematical model of the process to be controlled is not required and another is that a developed non-linear controller is obtained.

Therefore, it is well known that fuzzy logic is the beginning of artificial intelligence, since it is based on operating behavior and information on people’s habitual behaviors

Fuzzy control system:



Model-based Predictive Control (MPC)

In order to make the model-based predictive control model, we must take into account the process interactions and the difficult dynamics of the process to easily handle downtime, it has its application in systems with dead times and interactions between input and output variables.

For the process of identifying systems in the MPC, the process histories are taken where there are changes in the MV’s and their corresponding outputs to model as a matrix of transfer functions. For measurements previously only available through laboratory analysis or online analyzers.




MV: Manipulated variables

CV: Controlled variables

DV: Disturbance variables

Difference between PID and MPC

PID

MPC

Single input and single output controller

Control based on current error

Poor ability to handle delays and non- linearities

Little ability to handle disturbances and setpoint changes

Poor ability to handle constraints or constraints

Multiple input - Multiple output controller

Control of current and future deviations

Compensation for process delays and non- linearities

Optimal control for both disturbances and setpoint changes

Predictive constraint management

MPC Applications

Some of the most common multivariable predictive control applications are:

  • Oven control
  • Distillation column control
  • Compressor control
  • Boiler control
  • Dryer control

PlantPAx integrated with MPC



APC based on Artificial Intelligence

The evolution of APC and the incorporation of Advanced Data Analytics to Build an Intelligent Plant is increasingly present in the industry

The adopted procedure is as follows:

Response optimization:






ECN Automation
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