Maximizing Overall Profit Margin in Olefins Plant
with Real-Time Optimizer
Sukruethai Kamolsantiroj
Hydrocarbon Commercial and Optimization Department,
Rayong Olefins Co., Ltd. (SCG Chemicals), Thailand
sukruetk@scg.co.th
ABSTRACT
This paper covers Rayong Olefins’ implementation of the ‘Real-Time Optimizer (RTOP)’ system aimed at maximizing the overall
profit margin of the company. Rayong Olefins Co., Ltd. (ROC) has commenced its operations in 1999. Driven by the operational
excellence mindset, the production division has been taking several initiatives and managed to significantly reduce the conversion
cost, mainly in the areas of energy and utilities consumption, de-bottlenecking, process capability (Cpk) improvements, etc.
Despite the minimized conversion cost, ROC’s profit margin still depends largely on the gap between the unit price of feedstock
(raw materials) and that of each product. Since both the prices of the feedstock and products of ROC (i.e., ethylene, propylene,
hydrogen, mixed C-4, benzene, toluene, C8+, and CKB) are highly volatile and subject to fluctuations in the global market,
emphasis is placed on determining and producing the optimum product mix under current market situations as a means to improve
the overall profit margin in any given circumstances. In this regard, for monthly planning horizon, ROC implements offline
optimization for feedstock selection and production plan. For daily operations of the plant, the latest ‘Real-Time Optimizer’ or
‘RTOP’ system has been jointly implemented by ROC and Shell Global Solutions International in 2006 to further optimize the
plant-wide operations control so as to ensure the most profitable operating condition of the production process taking into account
all economic variables. Key challenges of the development of RTOP system that the development team had to overcome were the
high complexity of the plant-wide optimization model and the difficulty of keeping the optimization run time under one hour so
that the RTOP can make timely adjustments to the process. The result in 2007 has indicated significant benefit and short payback
period.
Keywords: Olefins Plant Optimization, Real-Time Optimizer
BACKGROUND
Rayong Olefins (ROC) is a subsidiary of SCG Chemicals, operating an upstream petrochemical plant in Rayong
Province, Thailand since 1999. The company is a large-scale supplier of ethylene (collectively called olefins), which is used by
downstream plants to produce polymers. ROC’s by-products include hydrogen, propylene, mixed C-4, benzene, toluene, C8+, and
Cracker Bottom (CKB).
The feedstock used for the manufacture of olefins in ROC plant is naphtha, which is available from sources around the
world in varying compositions. In ROC’s production process, naphtha is first heated by fuel gas combustion in the ‘Cracking
Furnace’ to temperatures at which large molecules of hydrocarbons crack into small ones, forming olefins and other by-products.
With the highest boiling point, CKB condenses in the ‘Quench Area’. The cracked gas then undergoes compression and
refrigeration transforming into liquid phase. Products are drawn off along the cascade separation process in which hydrocarbons
gradually regain temperature and each product reaches its boiling point. Benzene and Toluene are recovered through extractive
distillation process as by-products. C8+ gasoline comes off at the end of the production process, while all remaining hydrocarbons
go to the cracking furnace to be recycled.
Figure 1 – ROC's Production Process
In ROC’s path toward operational excellence, significant reduction in conversion cost has been carried out through the
implementation of ‘Total Productivity Management – TPM’, mainly in the areas of energy and utilities consumption, production
process de-bottlenecking, process capability (Cpk) improvements, etc. Nevertheless, ROC’s profit margin still depends largely on
the gap between the unit price of feedstocks (raw materials) and that of each product. Both the prices of the feedstock and products
of ROC (i.e., ethylene, propylene, hydrogen, mixed C-4, benzene, toluene, C8+, and CKB) are highly volatile and subject to
fluctuations in the global market, and different feedstock sources yield different proportions of each product. Emphasis is placed on
determining the optimum selection of feedstocks and product mix under current market situations as a means to improve the overall
profit margin in any given circumstances. Offline optimization of feedstock selection (monthly) and production plan (monthly and
daily) has already been implemented on the monthly planning horizon.
Despite previous improvements, rapid changes in actual market price of each product within periods shorter than the
planning horizon itself are still not accounted for by the optimization model. Moreover, another challenge remains in that there are
more than one operating condition of the production process from which to choose that coincide with a production plan. An
operating condition refers to a set of targets for process control parameters. One operating condition might consume less energy
and/or utilities than the others, resulting in a minimum conversion cost. For example, to increase ethylene product volume, two
options are available:
1.) Increase furnace coil outlet temperature by burning more fuel gas in the furnace, which results in higher yield; or
2.) Increase raw material feed rate while maintaining constant coil outlet temperature, which results in higher feedstock
volume requirement
Each option here consumes different amount of energy, thereby resulting in different amount of fuel cost, and produces
different amount of main products and by-products that sell for different prices.
In order to determine and establish optimal operating conditions for (hourly) plant operations, an additional production
process optimization system must be implemented.
SET UP THE TASK
The technical department sets out to develop a process optimization system to maximize profit margin. In general, there
are 2 types of process optimization systems, i.e. ‘Offline Optimization’ and ‘Real-Time Optimization’.
Table 1 –- Comparison of 'Offline Process Optimization' and 'Real-Time Process Optimization'
Optimization Procedure
Input Data
Process Control
Offline Optimization
Real-Time Optimization
Manual data retrieval,
staff’s judgements
required for final
decision
Daily Data
(yesterday’s data
for today’s
optimization)
Parameters adjusted
by operator
according to
optimization results
Automated
Real-Time Data
Parameters adjusted
by Real-Time
Optimization System
From Table 1, Real-Time Optimization approach is selected because of the dominating features in all criteria. It is
expected that the net profit should increase by about US$ 800,000 per year from the effect of process optimization.
DEVELOP THE METHOD TO PERFORM THE TASK
There are 4 general approaches by which a ‘Real-Time Optimization’ system can be implemented. Evaluation result of
all approaches is shown in Table 2 below.
Table 2 -- Alternatives to implementing 'Real-Time Process Optimization'
Approach
1. Develop own optimization module
2. Acquire and implement readily available optimization module
3. Hire solution provider for a turn-key project
4. Co-Develop and implement optimization module with vendor
Legend:
 -- Excellent
▲ – Fair
Time
Cost
Expected
Outcome
Technical
Knowledge
Obtained






▲






▲


 -- Good
 – Unacceptable
With an emphasis on expected optimization outcome (maximized margin) and technical knowledge obtained by the staff,
who will be operating and maintaining the system in the long run, ROC chooses to co-develop and implement the optimization
module with a vendor. To ensure commitment on expected outcome from the vendor, contract is awarded on the condition of
‘No Cure, No Pay’. Extensive process knowledge of ROC’s technical engineers coupled with vendor’s expertise in optimization
software is the key element to develop an effective solution. Having the technical engineers join the RTOP development team will
ensure that they will be able to handle the system in the long run.
The RTOP module shall be integrated into the existing platform of process control – the Distributed Control System
(DCS) and the Advanced Process Controller (APC) – see Figure 2.
Real-Time Optimizer (RTOP)
- Simulation Model
- Database
- Configuration Module
- Interface
Existing Process Control System
- Distributed Control System
(DCS)
- Advanced Process Controller
(APC)
Figure 2 Conceptual Model of RTOP Implementation on Existing Platform
EXPLORE SUCCESSFUL SCENARIO
Table 3 -- Risk-Avoiding Analysis for the Implementation of the Real-Time Optimizer System (RTOP)
Risk
Countermeasure
1.) Accuracy: Will the optimization model represent actual
operating condition of the plant?
- Technical engineers must ensure that all significant
parameters in each sub-process are identified and included in
the model.
- The model must undergo a series of simulation test and
fine-tuned with actual process characteristic.
2.) Robustness: Will the optimization model apply to all plant
scenarios?
- All operating scenarios must be taken into account while
formulating the model. Past data must also be thoroughly
reviewed.
- In the initial stage of implementation, care must be taken to
monitor for exception cases of plant operating condition.
Model adjustments should promptly be made in such cases.
(Quickly following the PDCA cycle.)
3.) Validity of Input Data: Can we trust that all input data are
actual process parameters?
- Acceptance test must be performed to validate all input
data from the plant.
- Maintenance plan of critical plant instruments, i.e. online
analyzers, must be reviewed so that their input reliability is
maximized.
4.) Optimization Speed: Will it take too long for solutions to be
calculated that APC just cannot achieve steady state before the
next optimization run?
- To keep the optimization runs at 6 effective cycles per day,
the model has to be streamlined to finish calculations within
1 hour and use the solution for the next 3 hours of operation.
The calculation sequence itself also has to be optimized.
5.) Plant Abnormality Handling: Will optimization create an
adverse effect in the presence of process abnormalities?
- The RTOP itself has to be able to detect process
abnormality and hold optimization sequence in such cases.
- The system must ask for panel operator’s
acknowledgement and approval before triggering the APC to
implement the solution. A ‘pop-up’ warning must be added
in the DCS controller screen to inform the panel operator.
- Panel operators must be trained so that they know when
and how to intervene the system to keep the plant in reliable
operating condition.
6.) User Friendliness: Will operators be able to use the system
with ease?
- Involvement of panel operators in the design phase of DCS
user interface for the RTOP system is required.
IMPLEMENT THE SCENARIO
Initially, the project schedule was planned for 2004-2006. However, since several capacity expansion projects were
carried out during the implementation of RTOP, subsequent modifications and adjustments to the simulation model had to be
performed to keep the accuracy, robustness, and optimization speed at proper levels.
Figure 3 Real-Time Optimizer Project Schedule
CONFIRM THE EFFECTS
The RTOP module was completed in May of 2007. From the comparison of ‘baseline’ and ‘optimized’ scenarios
recorded by the RTOP module in each loop at 4 hours interval, it is calculated that the project’s benefit amounts to US$ 985,088
per year.