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Chemical process development and optimization
The many benefits of a model-based approach
"We saved $5m on a single reactor contract"
— Korean chemical
company
There are many commercial benefits to be gained by applying model-based engineering and model-based innovation techniques to the design and operation of chemical processes.
Many of these 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
accelerated innovation and better-managed risk, leading to faster time to market
Our top 15 specific benefits are:
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.
Benefit: enhanced revenue; better ROI for capital invested.
Benefit: enhanced revenue; better ROI for capital invested.
"We were able to reduce lactose crystallisation batch time by 44%"
— Friesland Foods
Predict the formation of hot-spots and design them out to extend catalyst life
Better and more consistent product quality. By optimizing design and operating conditions it is possible to achieve better and more consistent product quality.
Benefit: higher prices for products; easier downstream processing (see below) leading to reduced costs and increased uptime; consistency leading to higher customer satisfaction.
Benefit: higher prices for products; easier downstream processing (see below) leading to reduced costs and increased uptime; consistency leading to higher customer satisfaction.
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.
Benefit: reduced downtime, lower catalyst lifetime costs, higher throughput and better overall quality product, resulting in a more profitable operation.
Benefit: reduced downtime, lower catalyst lifetime costs, higher throughput and better overall quality product, resulting in a more profitable operation.
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.
Benefit: lower capital and operating costs, resulting in a more competitive operation; lower lost revenue caused by downtime; less downstream capacity constraint on reactor production.
Benefit: lower capital and operating costs, resulting in a more competitive operation; lower lost revenue caused by downtime; less downstream capacity constraint on reactor production.
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.
Benefit: earlier revenue stream; less capital deployed; advantages of being early-to-market; more reliable start-up and initial operation resulting in improved economics.
Benefit: 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.
Benefit: 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.
Benefit: 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.
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.
Benefit: Better production, with higher throughput and product quality; lower catalyst lifecycle cost; reduced downtime; reduced energy costs, leading to more profitable and competitive process.
Benefit: 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 optimise future operating conditions to maximise the life of the catalyst [see article].
Benefit: Longer catalyst life; lower catalyst lifecycle cost; reduced downtime; better compliance with customer requirements, leading to more profitable and competitive process.
Benefit: Longer catalyst life; lower catalyst lifecycle cost; reduced downtime; better compliance with customer requirements, leading to more profitable and competitive process.
Improved controllability. Better designs, with lower design margin, result in a more well-defined operating envelope and better controllability.
Benefit: 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.
Benefit: 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.
Lower capital cost. The better designs made possible by rigorous modelling, generally result in lower design margin and hence capital cost.
Benefit: Lower capital deployed; less lifetime cost leading to more competitive product prices and better competitive position.
Benefit: Lower capital deployed; less lifetime cost leading to more competitive product prices and better competitive position.
Optimisation of steady-state operating conditions. A rigorous model allows easy optimisation of operating conditions such as reactor temperature and pressure, feed conditions, heating or cooling medium temperatures, etc.
Benefit: Better product throughput and quality with reduced energy costs, resulting in a more profitable and competitive process.
Benefit: Better product throughput and quality with reduced energy costs, resulting in a more profitable and competitive process.
Optimisation of transient operating conditions and operating policy. By applying dynamic optimisation techniques to a rigorous model you can optimise transient operations such as grade change, to minimise off-spec product and energy usage.
Benefit: 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.
Benefit: 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.
Optimisation of startup policy. Similarly, it is possible to optimise startup policy, for example to start up in the minimum time and with minimum production of off-spec product.
Benefit: Reduced process downtime; more flexible maintenance; less off-spec product and lower energy costs; higher process availability resulting in higher overall production.
Benefit: Reduced process downtime; more flexible maintenance; less off-spec product and lower energy costs; higher process availability resulting in higher overall production.
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.
Benefit: Rapid response potential; reduced environmental or safety impact; improved throughput and product quality resulting in better process profitability.
Benefit: Rapid response potential; reduced environmental or safety impact; improved throughput and product quality resulting in better process profitability.
Hot-spot determination. 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.
Benefit: extended catalyst life with reduced downtime; reduced catalyst lifecycle cost; improved throughput and product quality, all resulting in better process margin.
Benefit: extended catalyst life with reduced downtime; reduced catalyst lifecycle cost; improved throughput and product quality, all resulting in better process margin.
'Soft' benefits: knowledge, understanding and integration
There are a number of 'soft benefits' to modelling of chemical processes that should not be underestimated:
Greater understanding of the process and its operation. Modelling significantly increases process understanding, allowing better-informed decisions to be taken at all levels of design and operation.
Greater knowledge. The model-based data analysis and model validation exercises required to build a validated reactor model – in particular determining the definitive reaction set and establishing accurate kinetic parameters – lead to increased corporate knowledge of the process.
Integration of R&D and Engineering Design. The collaboration required for this modelling and validation effort, and the design of related experimental programmes, leads to a close integration of experimental work with the engineering modelling effort.
This means that experimental work is targeted at design, and design requirements can be used to guide experimental work.
This relationship can be formalised through the use of model-based experiment design to determine the optimal set of experiments, and the general application of Model-Based Innovation techniques.
More effective use of experimental data. For the reasons outlined above, experimental data can be leveraged and enhanced through its application in modelling
Familarity with the tools. Having access to, familiarity with, and understanding of the right toolset is critical for successful application. Without the right tools, the results cannot be achieved.
Confidence to innovate. The existence of a tool that provides accurate predictive information is a significant confidence booster when innovating.
Modelling can help achieve all of these
Advanced Process Modelling provides detailed, high-accuracy predictive information for quantitative decision support at all stages of the design and operation of reaction processes.
This means that virtually every design decision – from the choice of catalyst to the exact placement of baffles in a multitubular reactor shell, to the temperature and pressure at which a reactor is operated on a particular day – can be determined based on a precise analysis of the outcome.
Similar considerations apply to operating plant, where models can be deployed offline or online to generate value daily.
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