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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.
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).
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
Batch-based control of their hydrogen plant.
Modernization of the electrical cable insulation manufacturing plant
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:
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:
By having advanced control for your equipment or processes, you optimize your operation, which is reflected in the following:
Control of distillation towers
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
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
Some of the most common multivariable predictive control applications are:
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:
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