December 2024
Refining and Petrochemical Integration
Revolutionize the integrated refining and petrochemical industries with digital twin models
Today’s refining and petrochemical complexes are increasingly adopting digital model technologies. One such tool, digital twins, has proven to be an excellent solution for the design and operational stages. With digital twin models, plants can achieve optimized business sustainability, improving environmental impacts, asset performance and financial criteria.
The refining and petrochemical industries face major challenges, including the volatility of feedstock pricing, global energy transition targets, process intensifications, market diversifications and environmental regulations. Industry businesses are seeking optimized solutions to ensure their survival and sustainability. In addition, the massive volume of shared data in complex projects can be critical in plant operations. This results in uncertainties driving the need for modern approaches to optimize plant operations and attain sustainability requirements, without violating environmental regulations.
These factors dictate an optimization approach of material and energy management while controlling/modifying operating parameters of production units to ensure safe, economically controlled operating conditions. The increased volume of data processing within integrated industries, together with the ever-increasing complexity of processes, are leading to operational challenges, such as:
- Value chain optimization limitations
- Machine-learning (ML) difficulties with high data dimensions and time lags
- Maintenance planning limitations
- Continuous development requirements
- Environmental constraints.
Therefore, applying modern digitalization systems such as digital twin models will improve industry operations and economics, helping users to integrate ML tools and acquire real-time data.
The application of digital twin models. Digital twin models of physical assets provide seamless connections with real-time data across assets. Digital twins are process models connected through real-time data gateways and calibrated using test data to ensure energy and mass balance accuracy.
Digital twin model applications in industry are considered an integrated approach through engineering, procurement, construction, management and other key services related to supply chain and marketing activities. The expected benefits include:
- Access to key performance indicators (KPIs) that incorporate intrinsic operating parameters
- Production accounting systems that leverage mass and elemental balances to identify and address real losses within the production processes
- A supply chain planning system that updates optimization parameters
- Production optimization based on benchmarking parameters to improve gross margins
- Continuous real-time optimization to calculate gains by optimizing set points in real time.
Inputting operating parameters and feedstock specifications into a digital twin model can help operators understand how these factors affect optimized process operations outcomes. FIG. 1 shows a typical digital twin system configuration in petrochemical industries.
FIG. 1. A digital twin model application.
Digital twin models can continuously analyze industrial data and predict optimized operating parameters. Digital twins’ systematic operating sequences offer numerous benefits, including asset monitoring, recommended turnaround constraints and real-time optimization. They also provide optimization parameters for quality and quantity for integrated processes.
Many applications are considered, including visualizing KPIs for performance tracking, date reconciliation in production accounting, process optimization, equipment monitoring and system degradation. Therefore, applying digital twin models in a refining/petrochemical complex is considered an integral part of the complex’s digital structure and leads to optimized operational parameters. This study will consider KPI management, production accounting, and supply chain and process optimization.
KPI management. When tracking process plant equipment performance, KPIs act as a measurement of equipment performance. Traditionally, industrial information technology (IT) and operational technology (OT) have operational limitations since they have been considered separate entities with little crossover. Today, IT and OT interconnection provides process industries with valuable data to attain high KPIs.
This is based on the Industrial Internet of Things (IIoT), ML and loop design to exchange information between factory and digital models. End users can leverage existing assets and investments to drive a safer, more reliable and efficient complex enterprise. Digital twin models continuously calculate intrinsic parameters such as yield, feedstock limitations, energy consumption, equipment performance and emissions.
Since those models offer real-time data on critical parameters, their applications provide measurable progress compared to target parameters and insight into continuously improving individual assets throughout the entire complex. Therefore, KPI management will help organizations attain strategic goals, optimizing equipment and plant performance, and using calculations to address yields and process efficiencies to take advantage of improvement opportunities.
Production accounting. Digital twin models for production accounting provide accurate data on input limitations and loss control, since the system provides input material and energy balance and compares them to expected losses. This provides a systematic approach to reconciling data input errors.
Reconciliation entails distributing mass imbalance errors across streams and adjusting specific streams to achieve a close mass balance across the assets. Furthermore, process digital twin models enhance operational efficiency through various capabilities to ensure precise elemental balance and plant operating parameters.
Supply chain optimization. The process digital twin model plays an important role in integrated refining and petrochemical complexes, starting from feedstock selection, yields and economic criteria via applied optimization functions. Such models are used for planning, scheduling and optimizing assets.
They provide automated complex processes such as kinetic model calibration and validation and leverage artificial intelligence (AI) and ML methods to automate workflow, check equipment health and validate AI recalibration recommendations.
In addition, these models help to overcome suboptimal operational problems by continuously tracking asset performance, applying optimized operating targets, schedule enhancement aspects and inventory cost savings without violating environmental limitations. Therefore, applying digital twin models helps organizations monitor profitability, leading to positive business sustainability.
Process optimization. Real-time optimization (RTO) uses a digital twin model to identify the gaps between actual and benchmark performance during plant operations. These gaps are analyzed for corrective actions as follows:
- Debottlenecking
- Equipment/unit optimization
- Molecular management
- Margin improvement opportunities
- Environmental limitations.
With the application of advanced process controls (APCs) and digital twin models, the dynamic process is maintained at desired operating conditions by continuously reviewing process constraints, ensuring equipment availability, measuring economic parameters and eliminating process disturbances. Compared to traditional distributed control systems (DCSs), APCs help to implement desired set points from the RTO to achieve closed-loop optimizations where the optimizers communicate operational parameters to the APC. RTO set points require two models:
- The economic model: This is required for cost minimization while maximizing product yields
- The operating model: This is a steady-state process model that identifies operating limits for the process variables without violating environmental limits.
These models provide real-time visualization of the KPIs of each production unit, which will be considered a benchmark of the process conditions. Therefore, applying digital twin models bridges the gap between planned and actual operating parameters, updating model data inputs to accommodate variations of inputs and outputs.
This approach will result in extensive data analysis in production units, improving efficiencies while reducing data losses. It also provides reasonable approaches for process optimizations, energy consumption, carbon footprint and RTO.
CASE STUDY
A study was performed on a suspension polyvinyl chloride (S-PVC) reactor to maximize reactor production capacity based on the following design constraints:
- Initiator type
- Runaway reaction limitations
- Plant safety requirements
- Cooling capacity optimization
- Conversion percentage
- Product quality.
The main operating parameters of an S-PVC reactor are volumetric loading, chemical additives, temperature and pressure profile, and agitator load variations. In addition, initiator selection should be adapted for every specific case to avoid runaway reactions and/or off-specification (off-spec) products. Therefore, an optimum selection of initiator(s) can contribute to higher plant output and improved resin quality without reaching runaway reactions.
Reaction kinetics. In S-PVC polymerization, the reaction is considered isothermal. Since the heat generation rate steadily increases as the conversion does, a reaction profile tail peak is expected where the temperature exceeds the set reaction temperature point to attain maximum productivity.
Since the reactor cooling system has capacity limitations due to design configurations, more efficient control of the reactor heat removal along the reaction cycle was required. This could be realized by increasing the heat generated in the low-conversion region and decreasing the heat generated in the high-conversion region (e.g., reaction rate linearization with a proper initiator selection).
Initiator(s) selection is an optimized approach to improve productivity, considering heat transfer capacity, resin quality and safety requirements. For the evaluation of the non-cooling capacity utilization of the reactor, FIG. 2 depicts a relation between the vinyl chloride monomer (VCM) conversion rate and the polymerization time.
FIG. 2. The relation between VCM monomer conversion and polymerization time.
Initiator selection. In S-PVC polymerization, the reactor temperature ranges between 45°C and 70°C. Initiators with short or half-lives are considered if they meet the following requirements:
- Short polymerization times
- Problem-free handling and metering
- Satisfactory storage stability
- High-cost efficiency per kilogram (kg) of active oxygen
- Heat exchange capacity
- Required conversion
- Desired polymer production.
Initiator (half-life). The half-life is the time required for half of the molecules in an initiator to decompose at a certain temperature. The kinetic data of the composition of hydroperoxides are determined by measuring the active oxygen content in time. The constant rate of initiator dissociation is calculated by Eq. 1:
The kinetic data of the composition of hydroperoxides are determined by measuring the active oxygen content in time. The constant rate of initiator dissociation is calculated using Eq. 2:
“k” is the reaction rate constant, and “Ea” is the activation energy. Applying one initiator with a half-life requires additional steps to attain the maximum conversion rate and utilize the available cooling capacity of the reactor safely.
In this study, mixed initiators with different half-lives were considered to optimize the VCM conversion rates of the reaction in a safe and economic condition (FIG. 3).
FIG. 3. A comparison between half-life initiator curves and high active peroxides.
Mixed initiators. A mixed initiator concept is based on reactor design criteria, applying initiators with different half-lives and considering the following constraints:
- The efficiency factors for monomer and polymer-rich phases
- The rate of initiator decay after the pressure drop
- The reaction cycle and kinetics
- The optimized conversion rate.
The application of modern AI-digital twin models resulted in optimized reaction parameters that helped attain maximum productivity while maintaining product quality requirements. Based on available data on initiators and the applied kinetic reaction model, the selected initiator’s mix is as follows:
- Ethyl hexyl peroxy dicarbonate (EHP) (1 hr, half-life = 64°C)
- Cumyl peroxy neodecanoate (CPN) (1 hr, half-life = 53°C).
A study was performed on different concentrations of initiators to optimize reactor production capacity safely. The following are the proposed digital twin model parameters:
- Online monitoring of reaction rate
- Agitator load
- Reactor volume shrinkage
- Reactor temperature profile
- Reactor pressure profile
- Initiator type and concentrations
- Resin molecular weight.
- Production control
- Reactor cycle time
- Reaction rate constants
- Reactor pressure profile
- Reactor temperature profile
- Reaction time
- Volumetric loading
- Agitator load variations
- Injection water volume.
A few experimental trials have been conducted. FIG. 4 depicts the optimized polymerization curves using an EHP/CPN initiator mix with concentrations of 168 mg/kg and 375 mg/kg resin at 53°C.
FIG. 4. A comparison between a single EHP initiator and a mixed EHP/CPN initiators.
This optimized approach has led to the following outcomes:
- A reduction in reactor cycle time by an average of about 50 min per charge, increasing the specific productivity by an average of 15%
- An enhancement of product quality, especially the fisheye
- A smooth reactor heat profile, minimizing the reactor's build-ups
- An improvement in the pressure drop rate eliminates carryover problems in reactor overheads.
Takeaway. Today’s refining and petrochemical complexes are increasingly adopting digital model technologies. One such tool, digital twins, has proven to be an excellent solution for the design and operational stages. With digital twin models, plants can achieve optimized business sustainability, improving environmental impacts, asset performance and financial criteria.
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