ISO/TR 15746-4:2026

Automation systems and integration — Integration of advanced process control and optimization capabilities for manufacturing systems — Part 4: Application for distillation process

This document describes a solution for integrating advanced process control and optimization capabilities for manufacturing systems by introducing the advanced control system of the distillation column in detail with a separate distillation column as an application case. This document is intended to be used with ISO 15746-1, ISO 15746-2 and ISO 15746-3.

Systèmes d'automatisation et intégration — Intégration de contrôles de processus avancés et capacités d'optimisation des systèmes de fabrication — Partie 4: Application pour le processus de distillation

General Information

• Status: Published
• Publication Date: 24-Jun-2026
• ICS: 25.040.40 - Industrial process measurement and control
• Technical Committee: ISO/TC 184/SC 5 - Interoperability, integration, and architectures for enterprise systems and automation applications
• Drafting Committee: ISO/TC 184/SC 5 - Interoperability, integration, and architectures for enterprise systems and automation applications
• Current Stage: 6060 - International Standard published
• Start Date: 25-Jun-2026
• Completion Date: 25-Jun-2026

Overview

ISO/TR 15746-4 addresses the integration of advanced process control and optimization (APC-O) capabilities for manufacturing systems, focusing on practical application within distillation processes. Developed by ISO/TC 184/SC 5, this technical report serves as a reference for maximizing interoperability and integration across various automation platforms in the manufacturing industry.

By providing a detailed technical framework, ISO/TR 15746-4 supports consistent implementation of APC-O strategies, promoting operational efficiency, reduced redundancy, and real-time optimization. This document is especially relevant for process engineers, automation system integrators, product suppliers, and operations managers in the chemical processing and manufacturing sectors.

Key Topics

• Distillation Process Analysis: The report introduces the core principles of distillation in chemical manufacturing, highlighting its system complexity and the need for advanced control methods.
• Conventional vs. Advanced Control: Comparison between traditional PID-based control loops and APC-O solutions. It details limitations of conventional controls, such as susceptibility to measurement noise, limited adaptability to process disturbances, and cost-driven constraints.
• Technical Framework:
  • Soft Sensors: Implementation for real-time product quality estimation, reducing reliance on expensive on-line analyzers and time-consuming laboratory results.
  • Model Predictive Control (MPC): Usage of multivariable predictive models to achieve stable process operations, anti-disturbance control, and setpoint management.
  • Real-time Optimization: Automated adjustment of operational parameters to meet production schedules, quality requirements, and cost targets.
  • Performance Monitoring: Evaluation of both regular control loops and advanced controllers using quantitative and judgment indicators, and monitoring of key economic performance metrics.
• Information Exchange Models: Guidance on data structure and model definitions to enable seamless data flow among Level 2 and Level 3 enterprise systems, soft sensors, controllers, and optimizers.

Applications

ISO/TR 15746-4 is intended for use in:

• Chemical Manufacturing Facilities: Integrating advanced control and optimization for large-scale, multi-stage distillation columns.
• Automation System Integration: Facilitating interoperability between disparate control and management platforms from different suppliers.
• Operations Management: Supporting production planning, scheduling, and execution with real-time feedback and automated adjustments.
• Continuous Improvement Initiatives: Providing a standardized workflow for system lifecycle integration, encompassing requirement analysis, design, development, execution, support, and validation phases.
• Performance Validation: Establishing processes for evaluating and benchmarking APC-O system performance in distillation applications, ensuring stakeholder requirements are consistently met.

The report can assist organizations in aligning with global best practices, ultimately leading to reduced operational costs, enhanced production flexibility, and improved product quality.

Related Standards

• ISO 15746-1: General principles for integration of APC-O in manufacturing systems.
• ISO 15746-2: Information exchange models crucial for interoperability between automation and enterprise systems.
• ISO 15746-3: Frameworks and workflows for APC-O system lifecycle management.
• IEC Electropedia and ISO Online Browsing Platform: Official terminologies and definitions supporting standardization in automation and control.

These related standards provide foundational guidance complementing the specific application case for distillation processes detailed in ISO/TR 15746-4.

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Keywords: automation systems, advanced process control, APC-O, optimization, distillation process, manufacturing integration, process control, soft sensors, model predictive control, information exchange, ISO standards, chemical industry.

Standards Content (Sample)

Technical
Report
ISO/TR 15746-4
First edition
Automation systems and
2026-06
integration — Integration of
advanced process control and
optimization capabilities for
manufacturing systems —
Part 4:
Application for distillation process
Systèmes d'automatisation et intégration — Intégration de
contrôles de processus avancés et capacités d'optimisation des
systèmes de fabrication —
Partie 4: Application pour le processus de distillation
Reference number
© ISO 2026
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Analysis of the distillation process . 2
4.1 Introduction to the distillation process .2
4.2 Control and optimization .4
4.2.1 Regular control system analysis .4
4.2.2 Soft sensor and state estimation .5
4.2.3 Advanced process control .5
4.2.4 Real-time optimization .5
5 Technical scheme . 5
5.1 Technical framework for the distillation column APC-O system .5
5.2 Soft sensor .6
5.3 Advanced process control .6
5.4 Real-time optimization .7
5.5 Performance evaluation and monitoring .8
5.5.1 Evaluation of regular control performance .8
5.5.2 Evaluation of model predictive control performance .8
5.5.3 Monitoring of key economic indicators .8
6 Information exchange model . 8
6.1 Overview .8
6.2 Information exchange model L2 .8
6.3 Information exchange model L3 .10
6.3.1 Laboratory analysis data .10
6.3.2 Scheduling and optimization instructions .11
6.4 Information exchange model within APC-O .11
6.4.1 Overview .11
6.4.2 Soft sensor .11
6.4.3 Advanced controller . 13
6.4.4 Optimizer .16
6.4.5 Performance evaluation . .18
7 System lifecycle integration workflow .20
7.1 Overview . 20
7.2 Requirement analysis phase . 22
7.3 Design phase . 22
7.4 Develop phase. 22
7.5 Execution phase . . 25
7.6 Support phase .27
8 APC-O validation process .28
8.1 APC-O validation process for distillation column . 28
8.2 Typical validation indicators for the distillation column APC-O system . 29
Bibliography .32

iii
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee
has been established has the right to be represented on that committee. International organizations,
governmental and non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely
with the International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of ISO documents should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of any patent
rights identified during the development of the document will be in the Introduction and/or on the ISO list of
patent declarations received (see www.iso.org/patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT),see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 5, Interoperability, integration, and architectures for enterprise systems and automation
applications.
A list of all parts in the ISO 15746 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.

iv
Introduction
As a crucial part of the increasingly complex manufacturing systems, automation and control applications
which are enabled by advanced process control and optimization (APC-O) methodology and solutions are
implemented under the direction of production planning and scheduling. This task involves initially the
specific use of APC-O that will enable the integration of manufacturing operations management (MOM) with
the automation and control of manufacturing process and equipment.
Automation solutions equipped with both software and hardware components are provided by different
suppliers to accomplish APC-O functions. Due to the diversity of development environments and the
variety of demand focus, the automation solutions from various suppliers tend to be isolated and relatively
independent, which make it harder for the automation solutions to be integrated. Consequently, various
automation solution components that the customers can have access to would be filled with redundant and
duplicated functions, resulting in a waste of resources and limited interoperability. The proposed standard
offers a reference interoperability framework for advanced process control and optimization with the
intention of maximizing both the integration and the interoperability of automation solutions.
It is not the intent of this document to suggest that there is only one way of implementing APC-O or to force
users to abandon their current way of implementing APC-O.
The target users of this document include: users and providers of advanced process control and optimization
solutions, such as project solution suppliers, automation systems integrators, production departments of
companies, process engineers, independent software testing organizations, implementation and consulting
service organizations of advanced process control and optimization software, and relevant government and
academic organizations.
v
Technical Report ISO/TR 15746-4:2026(en)
Automation systems and integration — Integration of
advanced process control and optimization capabilities for
manufacturing systems —
Part 4:
Application for distillation process
1 Scope
This document describes a solution for integrating advanced process control and optimization capabilities
for manufacturing systems by introducing the advanced control system of the distillation column in detail
with a separate distillation column as an application case.
This document is intended to be used with ISO 15746-1, ISO 15746-2 and ISO 15746-3.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
advanced process control
APC
control strategy to cope with processes characterized by large time delays, non-minimum phase, non-
linearity, loop instability and multi-variable coupling
Note 1 to entry: APC enhances basic process control by addressing particular performance or economic opportunities
in the process.
EXAMPLE MPC, Adaptive control, Inferential control.
3.2
advanced process control and optimization
APC-O
collection of advanced process control and optimization strategies
3.3
workflow
sequence of activities with explicit starting and ending points to describe a task
Note 1 to entry: Workflows can also have branches, decision points, and events. A workflow is a type of activity model.

3.4
checkpoint
point where verification (3.9) and validation (3.8) activities needed to be performed throughout the APC-O
lifecycle
3.5
indicator
measurement of an aspect of the system or component
Note 1 to entry: There are two types of indicators: quantitative indicators (3.6) and judgement indicators (3.7).
3.6
quantitative indicator
indicator (3.5) that is calculated using the physical formula
3.7
judgement indicator
indicator (3.5) that is evaluated using the evaluation method
3.8
validation
process of evaluating an APC-O system to determine whether it satisfies the stakeholders’ requirements for
that system
3.9
verification
process of evaluating an APC-O system to determine whether the output of a phase satisfies the conditions
imposed at the start of that phase
4 Analysis of the distillation process
4.1 Introduction to the distillation process
Distillation is one of the most common unit operations in the chemical industry. Unfortunately, the research
on distillation has repeatedly been proclaimed to be a dead area, and some universities have even considered
to stop teaching the basics of McCabe-Thiele diagrams. However, there have been renewed interests for the
distillation process in recent years, mainly because the topic of distillation column has become a favourite
subject in the field of process systems engineering, including process synthesis, process dynamics and
process control. The distillation column itself is a system that can be viewed as a set of integrated, cascaded
flash tanks. However, such integration gives rise to a complex and non-intuitive behaviour, resulting in
a more challenging problem. The difficulty lies in understanding the behaviour of the whole system (the
distillation column), based on the given knowledge of the individual pieces (the flash tanks).

Figure 1 — Sketch of the distillation column
Table 1 — List of the variables in a typical distillation column
Name Definition
F
Feed flowrate [kmol/min]
DB,
Distillate (top) and bottoms product flowrates [kmol/min]
Distillate and bottom product compositions (usually of light component) [mole fraction]
xx,
DB
Condenser liquid level and bottom liquid level [m]
LL,
DB
Sensitive plate temperature [K]
T
R
P Top pressure [kPa]
Heat flowrate [kmol/min]
Q
H
Cooling flowrate [kmol/min]
Q
C
Reflux flow [kmol/min]
LL L
T N
tot
Boilup flow [kmol/min]
VV V
B1
N
Number of theoretical stages including reboiler
Total number of stages (including total condenser)
NN1
tot
i
Stage number ( i = 1 refers to the bottom stage; iN= refers to the feed stage)
F
Liquid and vapor flow from stage i [kmol/min]
LV,
ii
Liquid and vapor composition on stage i (usually of light component) [mole fraction]
xy,
ii
A typical two-product distillation column is shown in Figure 1. The most important notations are
summarized in Table 1 and a typical example of control loops for the distillation column is given in Table 2,
in which index i is used to represent the stage number, and the stages are numbered from the bottom ( i��= 1
) to the top (iN��= tot ) of the distillation column. Index B represents the bottom product and the distillate
product. Index j is used to represent the components;��jL=��refers to the light component, and jH��= refers to
the heavy component. Note that it usually refers to the light component, when there is no component index.

==
==
4.2 Control and optimization
4.2.1 Regular control system analysis
A typical example of conventional control system for the distillation column is shown in Figure 2.
Figure 2 — Typical example of conventional control system for the distillation column
Table 2 — Typical example of conventional control loops for the distillation column
No. Loop Loop description Control type
1 F Feed flowrate Regular control
2 L Reflux flowrate Regular control
3 Sensitive plate temperature Cascade control
T
R
4 Condenser liquid level Regular control
L
D
5 Bottom liquid level Regular control
L
B
6 Heat flowrate Regular control
Q
V
An example of conventional control loops for the distillation column is shown in Table 2.
The following problems often occur in the regular control system running process:
a) The abrasion that results in the decline or even the failure of the operation of control system will affect
the normal production process.
b) During the online operation process, the increase of measurement noise or even the failure of measuring
instruments will affect the regular production process.
c) The regular control system based mainly on proportional-integral-derivative (PID) law cannot
effectively solve the problems of large time delay and strong in the actual production process.

Apart from the above problems that are needed to be addressed during the daily operation of the plant,
there is also pressure as the result of the market competition. In response to the ever-changing market price
and demand, the need to continuously reduce the production cost and improve the operation efficiency of
the plant becomes inevitable and that is why the automation of plant operation becomes a key issue.
4.2.2 Soft sensor and state estimation
In the practical application of distillation column, the main measurement of conventional instrumentation
system includes the flow, the level, the pressure, and the temperature without the on-line measurement
of product quality. Due to the high cost of installing and maintaining an on-line quality analyser, the post-
test inspection can only be carried out through laboratory tests during the actual operation which is
characterized by a long laboratory testing cycle for about twice a day. For the above reasons, it is impossible
to obtain the real-time product quality variables during the production process, which will inevitably make
an impact on the production operation of the device.
4.2.3 Advanced process control
With reference to the regular control system, the closed-loop control cannot be achieved because of the
mechanism characteristics of the plant production process, the large time delay and the coupling between
the top temperature TD and bottom temperature TB. To deal with changes in output and production
demands, the operator needs to take a series of actions to switch between various working conditions,
which requires the advanced process control techniques.
4.2.4 Real-time optimization
Real-time optimization module implements the instruction from the scheduler to automate the production
process: in the case of meeting the needs of production management, the goal of operating the device
automatically can be achieved while maximizing the benefits.
5 Technical scheme
5.1 Technical framework for the distillation column APC-O system
Figure 3 — Technical framework of a typical APC-O system for the distillation column

As it is defined in ISO 15746-1, ISO 15746-2 and ISO 15746-3, a typical framework of APC-O system mainly
contains the following four parts:
a) Soft sensor: Indirectly calculating the unmeasured key indicators and variables such as the reflux
ratio, the product quality at the top and bottom of the distillation columns.
b) Multivariable model predictive control: Enabling smooth running and operation of the device using
model predictive controller(s), so that the measuring indicators, such as the liquid level and the temperature
of the device, can be steadily controlled around their predefined setpoints.
c) Real-time optimization: Automatically maximizing the benefits of the production process, with
respect to the system constraints related to the scheduling commands, the product prices, and the material
costs, etc.
d) Performance Evaluation: Evaluating and monitoring the performance of the basic PID control loops,
APC control loops, optimizer as well as monitoring the key economic indicators.
The technical framework of a typical APC-O system for the distillation column is shown in Figure 3.
5.2 Soft sensor
For the demand of product quality measure as well as avoiding the high cost of online quality analysis
instruments, the soft sensor instruments are set up to predict the real-time product quality in an online
manner. The specific variables to be measured in the distillation column APC-O system are listed in Table 3.
Table 3 — Variables to be measured in the distillation column APC-O system using soft sensor
No. Name Description Target
1 Top product quality (concentration)
X
B
2 Bottom product quality (concentration)
X
D
3 The ratio of the reflux flow L to top product flow rate D in
R
R
the column top return column, R = L/D
Soft sensor instrument establishes a soft sensor model using the available historical data and performs
the real-time prediction online. Moreover, the soft sensor instruments eliminate the deviations during the
online prediction process through the ground-truth laboratory analysis data.
5.3 Advanced process control
Table 4 describes a typical example of the process variables in the APC module using multivariable predictive
control. Table 5 describes the predictive models in the multivariable predictive controller for the distillation
column.
Table 4 — Typical example of process variables in the APC system of the distillation column
Process variables: MV\DV\CV
Name Description Target
L reflux flow Minimum movement control
MV
V boilup flows Minimum movement control
F feed flowrate
DV
Heat flowrate
Q
H
sensitive plate temperature Setpoint control
T
R
bottom temperature Setpoint control
CV T
D
reflux ratio Lower limit control
R
R
NOTE In this example, the multivariable model predictive control in the distillation column APC-O system is built
upon the low-level PID control system, thus the manipulated variables (MVs) of the APC-O system are defined as the
setpoints of the low-level PID controllers.
Table 5 — Predictive models in the multivariable predictive controller for the distillation column
Predictive Model:
T T R
R D R
L Model Model Model
V Model Model --
F
Model Model Model
Model Model --
Q
H
In this example, the goal of multivariable model predictive control is listed as follows:
a) Smooth running and operation of the device. For example, the top temperature T , the bottom
B
temperature T and the reflux ratio R are controlled according to the actual demands.
D R
b) Anti-disturbance: When the working condition of the device or the external operating environment
(such as the change of the treatment volume F ) changes, the device is automatically controlled with
good performance in this case.
5.4 Real-time optimization
In this example of applying APC-O in the distillation process, the real-time optimization module realizes the
automatic execution of scheduling instructions: The first step is to access the scheduling instructions issued
by production management; the second step involves the acquisition of operational data and the analysis
of operational status of the device; the third step is to optimize the variable parameters of the equipment
operations, and the fourth step is to verify the optimization results before proceeding with an optimization
instruction. The variables involved in real-time optimization is shown in Table 6.
Table 6 — Variables in the optimization module
No. Name Type Description Target
1 L Independent reflux flow constraint
2 V Independent boilup flows cost
3 F Independent feed flow rate cost, constraint
dependent bottom temperature constraint
4 T
B
dependent top temperature constraint
T
D
dependent reflux ratio constraint
6 R
R
7 B independent top product flowrate benefit
8 D independent bottom product flowrate benefit
dependent top product quality constraint
9 X
B
dependent bottom product quality constraint
10 X
D
Definition of the optimized parameters in this example:
device processing capacity: feed flowrate F
product production: product flowrate DB/
quality indicator: product quality/XX
DB
settlement price: feed, product flowrate DB/ , vapor consumption Q
H
Optimization target:
Max J = product flowrate D * price of product D + product flowrate B�* price of product B - vapor
consumption * price of vapor - feed flowrate�F * price of feed
Subject to:
the amount of feed (meeting the production requirements);
the product quality (complying with the standards);
ensuring that the device is operated smoothly during the production process.
5.5 Performance evaluation and monitoring
5.5.1 Evaluation of regular control performance
In this application case, the performance of the regular control loop is evaluated with the evaluation
indicators such as the commissioning operation rate, the stable rate, the control variance, and the controller
operating performance.
5.5.2 Evaluation of model predictive control performance
In this application case, the performance of advanced controller is evaluated with the evaluation indicators
such as the commissioning operation rate, the stable rate, the control variance, the controller operating
performance, and the model confidence.
5.5.3 Monitoring of key economic indicators
In this application case, statistical analysis of key economic indicators includes the indicators such as the
treatment capacity, the vapor consumption, the vapor consumption per unit capacity, the yield of product B,
and the yield of product D .
6 Information exchange model
6.1 Overview
According to ISO 15746-2, information exchange models are used for integration and interoperability
between APC-O systems, parts of an APC-O system, Level 2 automated process control systems, and Level 3
manufacturing operations management systems.
This chapter introduces an example of how we define exchange models L2, L3 and ones within APC-O in a
distillation process.
6.2 Information exchange model L2
While in the Execution phase, the APC-O system in this distillation process needs to read several data
artefacts from the traditional control system, or Level 2 systems. We used the pre-defined PID loop
information model as a standard method in the Level 2 system for the APC-O execution engine to access
data and events at runtime. Figure 4 is the information model for a PID loop that was used in this technical
report.
Figure 4 — PID loop information model defined in ISO 15746-2
Seven PID loops are included in this distillation process, shown in Table 7.
Table 7 — PID loop list of the distillation column APC-O system
No. Name Description
1 F feed flowrate
2 L reflux flowrate
4 condenser liquid level
L
D
5 heat flowrate
Q
H
7 bottom liquid level
L
B
As defined in ISO 15746-2, several properties are used to describe the PID loop. The top temperature (T ) is
D
one of the most important PID loops in distillation which highly relevant to the overall profit. In this technical
report, properties proposed in ISO 15746-2 are used to describe PID loops. The one of T using the template
D
is shown in Table 8 as an example:

Table 8 — Properties of PID loop T in this distillation column
D
PID Loop:  T
D
Name String
T
D
ProcessValue *** Double
SetPoint *** Double
Remote (APC-O) *** Double
Setpoint Limits: Minimun *** Double
Setpoint Limits: Maximum *** Double
Setpoint Limits: Rate of Change *** Double
Mode Manual/Auto/Remote String
Output *** Double
Remote (APC-O) Output *** Double
TrackingFlags *** Int
Vendor - Specific Attributes NULL
Output Limits: Minimum *** Double
Output Limits: Maximum *** Double
Output Limits: Rate of Change *** Double
6.3 Information exchange model L3
The L3 data format is specified according to the requirements of various advanced control and optimization
system of different modules. Note that such requirements are proposed by the actual process, not by this
document. This clause lists the required models and the data in detail.
6.3.1 Laboratory analysis data
Soft sensor module requires the laboratory to provide regular laboratory data on product quality. In this
application, the specific list is in Table 9:
Table 9 — Laboratory systems in this distillation column
No Name Process valu
...