Optimizing reactor design
Advantages and benefits of model-based reaction engineering
There are many economic benefits to be gained by applying advanced process modelling techniques to the design and operation of chemical reaction processes.
Through rigorous exploration of the design and operating decision space using accurate high-fidelity models validated against experimental or plant data, it is possible to accelerate new reactor design or improve the operation of processes that are already considered to be ‘optimized’.
Many of the benefits listed below can be achieved with little or no capital expenditure, little disruption to operation and very short payback time – in many cases only 6 to 12 months.
General benefits
In general terms, benefits are reflected in increased profitability and return on investment. They include:
- enhanced throughput and quality, leading to improved profitability.
- reduced capital or operating cost, leading to improved return on investment.
- lower energy cost leading to reduced carbon emissions
- better compliance with environment and safety obligations
- greater flexibility through the use of alternative feedstocks, leading to competitive advantage
- accelerated innovation and better-managed risk, leading to faster time to market
Specific benefits
In addition, there are many specific benefits of applying advanced process modelling:
| Key Performance Indicator (KPI) | Benefit | |
|---|---|---|
| Throughput and quality | ||
| Higher throughput. By optimizing reactor operating conditions or small aspects of the reactor design it is possible in many cases to achieve significantly enhanced throughput. | Enhanced revenue; better ROI for capital invested. | |
| Better and more consistent product quality. By optimizing design and operating conditions it is possible to achieve better and more consistent product quality. |
Higher prices for products; easier downstream processing (see below) leading to reduced costs and increased uptime; consistency leading to higher customer satisfaction. | |
| Improved downstream processing. By improving reactor product quality and consistency, less downstream processing is required to remove impurities and pollutants, and less material is recycled. |
Lower capital and operating costs, resulting in a more competitive operation; lower lost revenue caused by downtime; less downstream capacity constraint on reactor production. | |
| Innovation, process design and scale-up | ||
| Accelerated innovation with faster time-to-market. By using Model-Based Innovation and Model-Based Engineering techniques it is possible to evaluate design alternatives rapidly, integrate R&D experimental programmes with the engineering design, and start commercial production sooner. | Earlier revenue stream; less capital deployed; advantages of being early-to-market; more reliable start-up and initial operation resulting in improved economics. | |
| Easier and more reliable scale-up. Modelling – in particular hybrid modelling combining CFD hydrodynamic modelling with gPROMS advanced reaction modelling – is used with great effect in the scale-up of processes from laboratory to production scale. | accelerated development leading to better and more reliable process; earlier revenue stream and advantages of being early-to-market; better subsequent throughput and quality leading to more profitable process. | |
| Cost optimization | ||
| Lower capital cost. The better designs made possible by rigorous modelling, generally result in lower design margin and hence capital cost. | Lower capital deployed; less lifetime cost leading to more competitive product prices and better competitive position. | |
| Optimization of steady-state operating conditions. A rigorous model allows easy optimization of operating conditions such as reactor temperature and pressure, feed conditions, heating or cooling medium temperatures, etc. | Better product throughput and quality with reduced energy costs, resulting in a more profitable and competitive process. | |
| Operating policy, controllability | ||
| Optimization of transient operating conditions and operating policy. By applying dynamic optimization techniques to a rigorous model you can optimize transient operations such as grade change, to minimise off-spec product and energy usage. | Reduced off-spec product resulting in higher overall production, reduced downstream processing or recycling and lower energy costs; more flexible process allowing rapid response to customer demand. | |
| Optimization of startup policy. Similarly, it is possible to optimize startup policy, for example to start up in the minimum time and with minimum production of off-spec product. | Reduced process downtime; more flexible maintenance; less off-spec product and lower energy costs; higher process availability resulting in higher overall production. | |
| Improved controllability. Better designs, with lower design margin, result in a more well-defined operating envelope and better controllability. | More consistent quality product with reduced off-spec products and pollutants, leading to higher product revenue, lower energy costs, lower downstream processing costs and better environmental compliance. More flexible, profitable and competitive process. | |
| Catalyst | ||
| Hot-spot analysis. A rigorous model of a catalytic reaction process, particularly when linked to a CFD model for accurate modelling of the system hydrodynamics, can be used to predict the formation and location of potential hotspots. | extended catalyst life with reduced downtime; reduced catalyst lifecycle cost; improved throughput and product quality, all resulting in better process margin. | |
| Extended catalyst life. “Designing-out” of hot-spots in catalytic reactors means that (a) the catalyst lasts longer or (b) if desired, the reactor can be run at a higher overall temperature, aiding conversion and selectivity. | Reduced downtime, lower catalyst lifetime costs, higher throughput and better overall quality product, resulting in a more profitable operation. | |
| Catalyst ranking and selection. Rigorous catalytic reactor models can be used to rank and select catalysts based on their performance “in the reactor” and determine optimal loading profiles. | Better production, with higher throughput and product quality; lower catalyst lifecycle cost; reduced downtime; reduced energy costs, leading to more profitable and competitive process. | |
| Catalyst performance monitoring. Rigorous reactor models can be used to determine catalyst activity on a daily or weekly basis, and the information used to optimize future operating conditions to maximize the life of the catalyst. | Longer catalyst life; lower catalyst lifecycle cost; reduced downtime; better compliance with customer requirements, leading to more profitable and competitive process. | |
| Troubleshooting | ||
| Troubleshooting. A rigorous model, with its ability to predict what is happening inside a reactor to a high degree of accuracy, can be used for troubleshooting of many operational problems. | Rapid response potential; reduced environmental or safety impact; improved throughput and product quality resulting in better process profitability. | |
More Information
Cases
Multitubular reactors
gPROMS allows us to improve the performance of industrial reactors using our catalysts significantly
Publications
Industrial & Engineering Chemistry Research
From Laboratory to Industrial Operation: Model-Based Digital Design and Optimization of Fixed-Bed Catalytic Reactors
Predict the formation of hot-spots and design them out to extend catalyst life
Modelling answers questions such as:
- “Will catalyst A or catalyst B give me a better lifecycle return?”
- “How do I optimize catalyst loading for a fixed-bed reactor?”
- “What is the optimal startup procedure for this unit?”
- “What is the optimal grade-change policy when moving from product quality A to B?”
- “What is the optimal cooling medium inlet temperature?”
