WEBINAR ARCHIVE

This session describes how high-fidelity models containing deep process knowledge are coupled with plant data and automation systems to generate actionable insights in real-time. Digital applications in the form of performance monitors, soft sensors or RTOs (real-time optimizers) can be deployed in either advisory open-loop or a closed-loop mode with APCs, including nonlinear model predictive control (NLMPC). Applications can also predict the long-term impact of operational decisions on current operating severity / deactivation trends and provide operators with what-if analysis to test decisions before implementing them on the plant.

This session examines the digital design workflow for fixed bed catalytic reactors of various configurations. Consideration is given to optimization of reactor internal geometry taking into account operating conditions, and the verification of the final reactor design by coupling catalytic tube models with Computational Fluid Dynamics (CFD) models to examine the detailed interaction between the shell-side fluid hydrodynamics and tube-side reaction and heat transfer.

The digital revolution has significant implications for catalytic reactor design and operation. Catalytic reactors are at the heart of many processes, and their design and operation are often key to the process profitability. Given that the difference between a bad and good reactor design can mean hundreds of millions of dollars difference in profit over the lifetime of a plant, it is worth investing time and effort in optimizing the design for various anticipated operating conditions from the outset. This session describes the digital design workflow, from early-stage experimentation through reactor design and operational analysis, as well as deployment of models for everyday use in operations.

The steam cracking furnace is at the heart of maximizing ethylene production process profitability. Accurate information on key process indicators such as conversion, product yields, tube metal temperatures, and coil coking state and rates is essential for optimal operation. However measuring these quantities reliably in real time is often either impossible or very difficult. A further challenge is to operate the furnaces at optimal operating conditions to maximize profitability in the face of changing feedstock availability and composition over time, while taking into account plant constraints.

The purpose of the webinar is to provide an understanding of how to build a Digital Process Twin of an industrial catalytic reactor and how to get the additional value from the model-based design and operational optimization.

This webinar describes how the combination of equation-based optimization technologies and next-generation digital application framework enables easy application of fast, robust online site utility system optimizers.

Validating a process model with experimental data is a key part in ensuring that the model is both accurate and predictive across a range of scales. PSE has recently augmented its state-of-the-art model validation technology, significantly streamlining the validation workflow...

This presentation describes how the combination of equation-based general-purpose modelling technologies and next-generation digital application framework provide an environment for easy construction and application of fast, robust online utility system models. A real industrial example is presented where PSE’s tool gPROMS Utilities and PSE’s gPROMS Digital Applications Platform are used to develop and deploy a digital twin of a steam system...

Low-Density Polyethylene (LDPE) is produced at very high pressures in tubular or autoclave reactors. In the case of autoclave reactors, the properties of a particular LDPE grade produced depend on multiple operating decisions, such as operating pressure, set points for temperature in various zones of the reactor, and the choice and feed rate of chain transfer agents...

Complex multitubular reactors are at the heart of many chemical processes, and their performance is often key to plant profitability. However, operators often have little visibility of internal performance, relying instead on limited product stream instrumentation and after-the-event analysis. As the supply of raw materials and demand for products changes more quickly than ever before, operators adopt a conservative approach to ensure production. Without detailed insight into the reactor’s operation, money is left on the table.

Adsorption process development is typically a slow, iterative procedure where the goal is restricted to confidence rather than optimization. Engineers must rely on pilot plant testing and design heuristics because legacy simulation software is unsuited to the flow reversals, fast transients and discontinuities inherent to cyclic separations. Equation-oriented dynamic modelling can solve cyclic separation systems rapidly and robustly, allowing engineers to answer design and operational questions based on accurate predictions.

Digital design approaches are rapidly replacing traditional experimentation and pilot-based techniques in catalytic reactor design and operation. The ‘lifecycle’ new approaches use validated high-fidelity models to minimise experimentation time and reduce or eliminate the need for pilot plants; explore the decision space rapidly to result in optimal catalyst and reactor design combinations; quantify and design out uncertainty and risk; and use the models as online digital twins to monitor and optimize operation and provide operator decision support.This webinar provides a brief overview of the laboratory-to-industrial reactor approach, using workflows and methodologies that have been applied successfully over many years to many different fixed-bed catalytic reaction processes.

Cyclic separation processes are more important than ever to the chemical process industries. Gas-phase processes such as PSA, VSA and TSA enable air separation, hydrogen purification, CO₂ capture and many other applications. Liquid-phase processes such as SMB and liquid chromatography create high value-added products. The importance of cyclic separation processes creates a strong incentive for optimization.

Typically, distillation columns are designed and operated based on time-invariant (steady state) analysis. In reality however, distillation processes are continuously subject to disturbances, caused by variations in feedstock, changes in product requirements, the availability of utilities, and so on. Dynamic modelling is a key technology for optimizing column design and operation. It can be used to understand the effects of feed and product quality changes, periodic feed upsets and abnormal operation, and then to design and tune control schemes capable of dealing with such disturbances.

Reliable optimization of the design and operation of fixed-bed catalytic reactors requires highly detailed models that can represent the relevant phenomena at all scales – from microscale reaction and diffusion in the catalyst pores to macro-scale operation in the full industrial reactor geometry. PSE’s Advanced Model Library for Fixed-Bed Catalytic Reactors provides detailed catalyst and packed bed models for heterogeneous catalysis, plus cooling models for tubular and annular reactors, to allow high-fidelity modelling of virtually any kind of fixed-bed reactor.

The performance of styrene plants is highly dependent on chosen operating conditions, the availability of utilities and current market price of ethylbenzene, styrene, toluene and benzene. Styrene production plants are typically integrated facilities, with complex reactors, significant material re-cycles and heat integration, which poses considerable challenges for process optimization. This webinar explains how a rigorous approach based on advanced process models, now available off-the-shelf in the gPROMS Process advanced process flowsheeting environment, can help identify real additional value in operation.

Plants consume fuels, electricity and steam in time-varying quantities, and via multiple routes. The process plants themselves can generate steam and fuels, electricity can be sold to the grid and the prices of the various components can change hourly. Added to this complexity are day-to-day changes in major equipment availability. PSE’s gPROMS Utilities suite helps find a way through the maze by applying rigorous optimization technology to process models of the utility system, taking into account demands, prices and availability, to find the economically optimal operating profile for any situation.

Accurate prediction of physical properties is an essential component of process modelling. The advent of equations of state derived formally from molecular-level interactions, such as those based on the Statistical Associating Fluid Theory (SAFT), marks a step change in this context. This webinar demonstrates the application of gSAFT to an issue of particular relevance to the pharmaceutical industry: solubility prediction. Topics include:

• Predictive capabilities of gSAFT for polymer solutions
• Defining polymer mixtures in gSAFT
• Accurate and reliable phase equilibrium calculations for mixtures involving polymers
• Reactor modelling: modifying the polymer structure during process simulation: variable molecular structures in gSAFT

The dynamic nature of batch processing makes process optimization a challenging task unsuited to traditional steady-state simulation tools. Model-based engineering empowers the engineer to explore, evaluate and optimize alternative operating policies, making it possible to enhance product quality, minimize batch time to increase throughput subject to quality and other constraints, and optimize recipes for different equipment or product qualities.

Compression trains affect the efficiency and profitability of many industrial sectors including Chemicals, Oil & Gas and Carbon Capture. This webinar describes how Advanced Process Modelling can help optimize compression train operation. Through detailed dynamic modelling, different anti-surge control configurations can be tested, taking into account interactions with other control system and process units. The presentation also describes an approach to determine optimal start-up and shut-down procedures. Compression train optimization has been shown to increase throughput by 20% and reduce energy consumption by 10%.

The advent of continuous manufacturing in the pharmaceutical industry has sparked an increased interest in advanced process understanding, digital design, and modernising pharmaceutical manufacturing technology. Mechanistic model-based approaches have been developed and implemented to facilitate risk assessment, design space exploration, and Quality-by-Design in the emerging field of continuous drug product manufacturing.

In the first part of this work, we examine a continuous cooling crystallization cascade process and explore its optimization, while maintaining the content uniformity of the final solid oral dosage form. A number of optimization objectives are then posed, which include increasing throughput of the API and impact of continuous seeding. In the second portion, a model-based workflow for the scale-up of filtration is presented. The workflow begins with the estimation of the filtration properties from small scale pressure filter experiments. The cake properties were subsequently employed within a centrifuge model to predict the filtration behavior of the system at plant scale and to optimize the process schedule.

Traditionally, formulation and process development within the pharmaceutical industry is driven by extensive trial-and-error experimentation. The Digital Design of Drug Product (D3P) project integrates qualitative and quantitative tools to facilitate an approach to formulation and process design based on the development of fundamental process and product understanding. A global system analysis approach has been used to realise an in-silico ‘design space explorer.’ This tool is applied to a roller compaction tablet manufacturing process to understand the impact of raw material and process changes on product quality. The approach can be used to inform risk assessment and to design a robust control strategy for the product.

Accurate prediction of physical properties is an essential component of process modelling. The advent of equations of state derived formally from molecular-level interactions, such as those based on the Statistical Associating Fluid Theory (SAFT), marks a step change in this context. This webinar demonstrates the application of gSAFT to an issue of particular relevance to the pharmaceutical industry: solubility prediction. Topics include:

• Theoretical background for prediction of solid/liquid equilibria
• Gibbs free energy models for solid phases
• Solubility predictions of complex molecules in pure and multicomponent solvents, with particular emphasis on active pharmaceutical ingredients

In vivo drug precipitation has been a major issue facing poorly soluble drugs, especially weak bases. This work aims to provide a framework on how to use the Artificial Stomach Duodenum (ASD) System to assess the extent of precipitation of poorly soluble compounds under biorelevant conditions. The concentration profiles measured in the ASD System are used as inputs in the gCOAS® ASD model to estimate the nucleation and growth kinetic parameters. These crystallization kinetic parameters are then used in the gCOAS GI tract model to simulate in vivo precipitation under human fasted conditions and to calculate the fraction absorbed as a function of dose.

This webinar presents how Systems-based Pharmaceutics is transforming pharmaceutical process development and how it will continue doing so in future. Topics include:

• The Systems-based Pharmaceutics vision
• How industry and academia are collaborating to make this vision a reality: SbP Alliance, D3P, REMEDIES and ADDoPT
• Industrial case studies illustrating use of systems approaches in drug substance manufacture, drug product manufacture and in-vitro / in-vivo drug product performance

This webinar explores the high fidelity (or mechanistic) mathematical models which underpin the oral absorption and in vitro dissolution and precipitation simulations, in the context of case studies presented in previous gCOAS webinars. Topics include:

• Population balance tracking of particle size distribution
• Detailed, mechanistic precipitation and dissolution models
• And more…

The biopharmaceutics performance of drug products can depend on multiple factors, including drug substance physicochemical properties, particle size distribution, patient physiology and dosage form composition. Even to the most experienced investigators, the interplay of these factors is not intuitive. In silico modelling allows for the low cost, rapid identification of risk factors affecting drug absorption. Formulators can then develop risk mitigation strategies before clinical trials, allowing them to design clinical studies to understand, confirm and mitigate the predicted risks.

As the pharmaceutical industry faces increasing pressure to bring new and better drugs to market earlier, formulation scientists are challenged to reduce expensive, time-consuming animal and human trials. Accurate predictions of oral absorption based on physicochemical properties can help identify and mitigate formulation risk factors prior to FIA trials. This webinar presents a case study of mechanistic oral absorption simulation for the drug ziprasidone , a hydrochloride salt demonstrating a number of challenges for effective formulation. The presentation is based on work done in gCOAS, a new analysis tool for a priori prediction of drug absorption.

Advanced process modelling is being used to increase the robustness of chromatographic bioseparations in the face of process variability. This presentation describes how the development and validation of a mechanistic model of a chromatographic separation where resin lot variability can cause significant performance issues. The model was successfully applied to enable process operation further away from high risk regions, to increase the size of operating regions and improve flexibility to variations in process inputs.

Systems-based Pharmaceutics (SbP) is a holistic approach to the development and optimization of drug manufacture and drug delivery. It applies high-fidelity mechanistic models validated against experimental data to increase R&D efficiency (making better use of PAT), perform smarter and faster tech transfer (fewer large scale trials) and ultimately design optimal processes using an industrially validated QbD approach. SbP has the potential to transform pharmaceutical process development.

By developing mechanistic models and customizing some of them at each step of the manufacturing process of a drug product at laboratory scale, we propose an interconnected model using gPROMS software which supports the process scale-up linked to critical quality attributes of the final tablet drug product. Simulations performed help to predict operating domain and impact of variation of input parameters on the Critical Quality Attributes. We achieved to secure first clinical batch at pilot scale without any technical batch and saved time, resources, and material.

Mechanistic modelling of spray dryer performance on milk powder moisture content would attribute to the efficiency, understanding of spray drying and improving the consistency of the powder moisture content. The next step in modelling is to bring these models into real time calculation and make them integral in factory operations. With a digital twin this is exactly what is being done. We introduce a mechanistic model that extract data from the sensors, makes a powder moisture prediction and communicates in real time with the process automation. Such a Digital Twin reduces the need of manual data input that is sensitive to errors and at the same time increases factory output and reduces variability.

In this webinar, we will discuss Siemens PSE’s vision for how model-based solutions can bring value addressing industrial challenges in continuous chromatography process development and operation. We will then focus on case studies where model-based solutions have provided value to industry.

In this webinar, we will discuss Siemens PSE’s vision for how hybrid bioreactor models can bring value addressing industrial challenges in upstream process development and operation. We will present a case study where the Mechanistic-ANN approach has been adopted to complement existing mechanistic knowledge.

In this webinar, we will provide an overview of the technical approach adopted at Siemens PSE for the creation and application of end-to-end bioprocess digital twins and discuss how they can provide value through a number of industrial case studies.

In this webinar, we will discuss how Siemens PSE’s model-based solutions can bring value and help address industrial challenges in active ingredient manufacture process development and operation in the specialty chemicals & agrochemicals industries by focusing on several case studies. In these case studies, we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

In this webinar, we will overview the challenges and needs of the dairy industry and discuss how these can be met through digitalization of dairy manufacturing assets with predictive process digital twins. We will discuss how leading dairy organizations are using process digital twins in gPROMS Formulated Products to drive innovation and how they are deploying these digital twins into manufacturing to realize multiple operational benefits day-to-day. Such organizations are adopting these approaches as a core part of their innovation and manufacturing strategy.

The emergence of system based modelling for pharmaceutical manufacturing processes is providing an opportunity to holistically model the influence material and process parameters on intermediate and end product quality attributes. These type of models are in effect a science-based digital twin of an entire manufacturing process.

In this webinar, we will overview the industrial challenges in the Food & Beverage industries and discuss Siemens PSE’s vision on how web-based deployment of model-based solutions can support addressing them. We will be joined by Maykel Verschueren and Diana Carrero, R&D specialists from the Global Process Technology department at FrieslandCampina. They will explain how gPROMS, and the gPROMS Web Applications Platform in particular, are used within FrieslandCampina to facilitate the deployment of models developed by R&D experts by end users in supply chain.

Here we present a ‘Digital Twin for Integrated Continuous Manufacturing of Synthetic Drug Substance’ via systems-based modelling. As a part of this methodology, process characterization was executed to enable mechanistic understanding and modeling of the physical and chemical phenomena and to obtain quality data that are fed into model development.

Siemens Process Systems Engineering new bioreactor module in gPROMS FormulatedProducts has the potential to change the way bioreactor digital twins are developed and maintained in the industry. Experiments were replicated from Biogen’s mAb development in simulation of cell growth, death, and lysis. The consumption and secretion of numerous amino acids was modeled. An optimized feeding schedule to maximize growth with constraints was performed. The optimized experiment was performed, and the results showed the need for iteration in model development and experimentation and the need to question assumptions in the model. Future work will attempt to incorporate model learnings from the experiment.

Sanofi is investing in modular concepts with a high capability of production of API and the ability of infinite reconfiguration. We are not only a thinking machine; to enhance our ability to always produce the best quality drugs for our patients, we are also implementing a digital twin system, tracking and controlling the quality of our product at any moment of time.

In this webinar, we will discuss Siemens PSE’s vision for how model-based solutions can bring value addressing industrial challenges in bioreactor process development and operation. We will then focus on case studies where model-based solutions have provided value to industry. In these case studies, we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

WB Salt liaised with Siemens Process Systems Engineering (“SPSE”) to use mechanistic modelling to quantify the effect of the uncertainties in the environment and operating conditions on the salt field crude salt production capacity. The modelling work was carried out using the solar pond model libraries within SPSE’s digital design and digital operation environment, gPROMS FormulatedProducts. This modelling environment provides users with the necessary tools to configure and calibrate the digital twin of the process and subsequently deploy it for off-line and on-line usage.

In recent years, mechanistic models have been applied to a broad range of drug product manufacture applications in pharmaceutical R&D and engineering, improving efficiency through reducing experimental requirements, facilitating scale-up and tech transfer, and enabling virtual design space exploration. The use of mechanistic models to aid design and operation of continuous manufacture is well known due to regulatory and industry trends. This webinar will demonstrate how gPROMS FormulatedProducts can also be used to build models of integrated batch drug product manufacturing processes and how it can be used to describe and predict the quantitative behavior of pharmaceutical processes.

In recent years, mechanistic models have been applied to a broad range of drug substance manufacture applications in pharmaceutical R&D and Engineering, improving efficiency through reducing experimental requirements, facilitating scale-up and tech transfer, and enabling virtual design space exploration.

In this webinar, we will overview the industrial challenges in chromatography and discuss PSE’s vision on how model-based solutions can support addressing them. We will then focus on several industrial case studies where model-based solutions have provided value to industry. In these case studies we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

In this webinar, we will overview the industrial challenges in spray drying and discuss PSE’s vision on how model-based solutions can support addressing them. We will then focus on several industrial case studies where model-based solutions for spray drying have provided value to industry. In these case studies we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

In this webinar, we will compare and contrast science-based modeling frameworks for drug product manufacturing processes. Industrial applications of these approaches will be discussed, highlighting key differences in assumptions, data requirements, capabilities, and limitations. These cases studies will cover a variety of unit operations, including roller compaction, continuous feeding and blending, tablet coating, and fluid bed granulation. Finally, considerations will be presented to determine how to select the most appropriate model to solve a particular problem.

This webinar will present an overview of the modelling capabilities of PSE modelling tools of interest to the Specialty chemical & Agrochemical industries. User case studies illustrating the application of our mechanistic modelling tools to industrial cases will also be provided by users from Syngenta and Solvay.

In this webinar, PSE’s mission for the Food, Beverage and Consumer Goods industries and solutions will be discussed with a focus on the dairy processing industry. The webinar will look at example dairy use cases where digital design and digital operation is adding significant value to major organizations.

In this talk, case studies within the pharmaceutical sector will be presented to demonstrate the use of mechanistic models in the pharmaceutical industry across the lifecycle – from R&D to operations. Recent advances in integrating mechanistic models with online control and monitoring interfaces will be presented. Finally, strategies will be discussed to increase the adoption of these tools in the pharmaceutical sector, leveraging knowledge from R&D to enable online control.

Digital transformation is a dominant trend in the process industries to address profitability and productivity goals. Advanced process modeling and simulation play a central role in both digital design and intelligent digital operations, by capturing deep process knowledge in a way that can be leveraged within Digital Twins to generate value over the lifetime of the process.

Digital transformation is a dominant trend in the process industries to address profitability and productivity goals. Advanced process modeling and simulation play a central role in both digital design and intelligent digital operations, by capturing deep process knowledge in a way that can be leveraged within Digital Twins to generate value over the lifetime of the process.

This webinar will briefly introduce various Digital Twin concepts throughout the lifecycle but focus on the Performance Engineering for simulating the manufacturing processes.

This webinar will introduce various Digital Twin concepts throughout the lifecycle of manufacturing in the pharmaceutical industry.

In this webinar, PSE’s mission for the Food, Beverage and Consumer Goods industries and solutions will be discussed with a focus on the dairy processing industry. The webinar will look at example dairy use cases where digital design and digital operation is adding significant value to major organisations.

In this webinar, we will compare and contrast mechanistic model-based approaches to QbD with traditional DoE-based approaches. The concept of model quality will be presented, and the verification and validation framework will be presented, alongside techniques to quantify model uncertainty. A standard workflow will be introduced to validate a mechanistic model (calibration and blind testing), characterize parametric uncertainty, and analyze the effects of that uncertainty in the resulting model predictions. A simple continuous direct compression process will be used to illustrate these concepts, where feeder and blender models are calibrated and the impact of the uncertainty in those parameters is explored.

Spray drying is widely used across many industries to create diverse products with a broad scope of functionality such as detergents, food powders, ceramics and pharmaceuticals. By utilizing digital design tools alongside characterization experiments, efficiency can be achieved both in the design of the spray drying process as well as in the operation. This webinar will practically demonstrate the workflow that has been successfully utilised to achieve savings in the real world.

he proportion of scientific evidence supporting medical product regulatory applications derived from modeling and simulation studies is expected to continue to grow into the future. In the Quality by Design framework, mathematical models can and have be utilized at every stage of product development and manufacturing. Thus, the regulatory assessment of product quality models is not unprecedented but the frequency, types of models, and applications are evolving. This evolution is being driven in part by the adoption of advanced manufacturing such continuous pharmaceutical manufacturing.

Model-based scale-up and optimization is a powerful technique for achieving the desired product quality and reducing the cost of experimentation and the time to market. For complex crystallization processes, population balance modelling is capable of predicting the effect of the batch recipe on the particle size and shape distribution. However, this technique is mathematically complex and has traditionally required deep knowledge of specialist areas. New tools allow engineers to apply population balance modelling without highly-specialised numerical skills. This webinar will present a case study illustrating the application of a mechanistic morphological crystallizer to allow for the prediction of particle size and shape during a crystallization process.

Drug product performance models are applied across the development lifecycle, supporting preclinical activities, through to pivotal trials and post approval changes. Drugs are delivered in many different dosage forms with complex structures, materials and behaviours, yet scientists are often limited to using models that only cover the properties of the API and which have minimal description of the formulation and its effect.

Spray drying is widely used to create powders with a broad scope of application such as detergents, food powders and pharmaceuticals. Combining mechanistic models with targeted experiments enables efficient development of robust spray drying processes. This webinar will examine the workflow needed to utilise a combined approach.

gPROMS FormulatedProducts^® is PSE's platform for the design and optimization of formulated products and their manufacturing processes. It allows scientists and engineers to screen formulations for end-user attributes, determine whether they can be manufactured efficiently, and explore the design space of the whole formulation and manufacturing chain.

Model-based scale-up and optimization is a powerful technique for achieving the desired product quality and reducing the cost of experimentation and the time to market. For complex crystallization processes, population balance modelling is capable of predicting the effect of the batch recipe on the particle size distribution (PSD) of the final product. However, this technique is mathematically complex and has traditionally required deep knowledge of specialist areas. New tools allow engineers to apply population balance modelling without highly-specialised numerical skills.

Lyophilisation (or freeze-drying) is an important unit operation of pharmaceutical, biopharmaceutical and food technology allowing drying of heat sensitive products at low temperature. Several research groups and industrial companies have developed, tested and published mathematical models of the primary and secondary drying based on physical laws. There is a need for lyophilisation models written in a modern, user-friendly programming language allowing fast parameter estimation, automatic graphical outputs and parametric sensitivity studies.

Polymorphic transformation greatly increases the difficulty of design and optimization for crystallization processes. This webinar shows how gCRYSTAL^® allows scientists and engineers to efficiently and accurately model the effects of process variables such as seed size, mass, contamination level and agitation rate.

High-fidelity predictive modeling of crystallization processes allows design and operating decisions to be based on accurate information obtained by combining fundamental physics and experimental or operating data.

Accelerate scale-up with a low-cost, low-risk procedure that minimizes the time required for experimentation.

Applying gCRYSTAL^® to a process involving multiple Mixed Suspension Mixed Product Removal (MSMPR) crystallizers.

The climate crisis is driving significant change in the energy and chemicals sectors with new process development and adaptation of existing processes to accommodate new feedstock and energy sources. Bringing these processes to market rapidly poses both opportunities and challenges for organisations leading the energy transition. This webinar showcases the Siemens gPROMS digital design technology that is helping the green hydrogen industry to significantly reduce time-to-market for new hydrogen processes.

With many of the required transformative technologies still in their infancy, there is a need for solutions that support a reduction in greenhouse gas emissions in the immediate future. Carbon capture, utilization and storage (CCUS) can significantly help reduce emissions from today’s fossil-based processes. It is also particularly relevant technology for high-temperature chemical processes for which there are few alternatives.

In compliance with international climate agreements, and with CO₂ emission costs expected to rise, advanced economies around the world are rapidly putting in place strategies for decarbonization. Hydrogen is widely expected to play a key role in this transformation, and technologies for its production and utilization are seeing an increased interest.

This seminar explores how the nuclear sector and the industry in general can benefit not only from shared, standardised modelling techniques and approaches, but also the introduction of best practices from other industry sectors. This can help to accelerate innovation while ensuring a consistent and co-ordinated approach between R&D, operators, consultancies and engineering companies. The theory is illustrated with current process modelling applications from organisations such as Sellafield Ltd, National Nuclear Laboratory, DBD, EDF-Cyclife and WRPS. The International Atomic Energy Agency will give an overview of their strategies, direction of industry trends and provide their take on the necessity for adopting advanced process modelling.

This webinar will cover how state-of-the-art model-based engineering techniques can used to guide R&D efforts in CO2 capture, reduce Capital Expenditure of CCS projects and optimize dynamic operation of CCS chains.

Whole-chain System Modelling for Carbon Capture and Storage
Design and operation for safe, commercially-viable CCS chains involves many techno-economic trade-offs between diverse stakeholders. In order to proceed to commercial-scale operation with confidence, many questions need to be answered, with decisions based on accurate quantification. System-wide modelling is the key enabling technology for such analysis. This webinar demonstrates the benefits of system modelling to owner-operators, process vendors, engineering companies and equipment vendors.

Compressor trains are typically designed in an iterative fashion using heuristics rather than true optimization techniques. Commercially-viable Carbon Capture & Storage chains will require flexibility and economic optimization that typical design techniques cannot deliver. Model-based economic optimization accurately quantifies the effects of design and operational choices, and replaces the current iterative design practice with a single-step procedure.

The purpose of the webinar is to provide an understanding of how digital design tools and techniques are being applied to bridge the gap between R&D and operations in the design of electrolyzers, resulting in faster time-to-market and optimized component and system design.

Fuel cell designers and system manufacturers face the challenge of interpreting large volumes of experimental data in order to design and optimize their products. This webinar focuses on how data can be processed to uncover precision details of cell, stack and system performance. The complementary use of experimental data and validated mathematical models in the life cycle of fuel cell system design brings to light design limitations and provides a basis for more effective experimentation and optimization.

Wastewater systems optimization based on high-fidelity process models has been shown to reduce plant energy consumption by up to 40% while maintaining water purity standards. This webinar demonstrates how plant operators can run efficient, effective industrial and urban wastewater treatment plants that can adapt to any change in incoming pollutant load, power and chemical costs, effluent purity standards or environmental regulations. It also illustrates how operators can accurately determine the effect on plant operation of upgrades and retro-fits, using the retro-fit of a bio-filtration process as an example.

Wastewater systems optimization based on high-fidelity process models has been shown to reduce plant energy consumption by up to 40% while maintaining water purity standards. This webinar demonstrates how plant operators can run efficient, effective wastewater treatment plants that can adapt to any change in incoming pollutant load, power and chemical costs, effluent purity standards or environmental regulations. It also illustrates how operators can accurately determine the effect on plant operation of upgrades and retro-fits, using the retro-fit of a bio-filtration process as an example.

Many companies have valuable corporate knowledge captured in legacy process models. Some models are written in general purpose programming languages such as FORTRAN or C++, some are in overextended spreadsheets, and others are held hostage in simulation environments that are no longer actively supported. This webinar provides a step-by-step approach to move valuable models from legacy applications to a modern modelling platform.

This webinar describes how large-scale MINLP optimization can be used to maximize the profitability of complex chemical plants, both at the engineering design stage and during operation. One of the two cases illustrated here led to identified savings of 10s of millions of euros per year. In the production of chemicals such as propylene oxide and styrene monomer, the annualised cost of the process (and hence its profitability) is strongly influenced by the capital cost of the reactor and separation equipment, and the energy requirements for large distillation columns. Thus when determining the optimal process design, it is essential to consider all key decisions simultaneously, including both the reaction and separation sections.

The main use of process simulation and modelling tools is to analyse "what-if" scenarios in order to improve process design and operation. Currently this is mostly done manually as a point activity, using repeated simulation runs. gPROMS’ global system analysis (GSA) represents new technology to systematically and comprehensively quantify the effect of uncertainty in the operation and design of chemical processes at large scale. It allows users to quickly and easily assess the impact of uncertainty in input factors on key outlet responses in a system in order to explore the design or operational space rapidly and effectively, reduce uncertainty, manage risk in process innovation and support investment decisions.

Validating a process model with experimental data is a key part in ensuring that the model is both accurate and predictive across a range of scales. PSE has recently augmented its state-of-the-art model validation technology, significantly streamlining the validation workflow.

gPROMS Process is a next-generation Advanced Process Modelling environment for optimizing the design and operation of process plants. Process combines industry-leading steady-state and dynamic models with all the power of the gPROMS equation-oriented modelling, analysis and optimization platform in an easy-to-use process flowsheeting environment. This webinar will show how gPROMS Process is transforming the way the process industries design and operate by unlocking new value and competitive advantage in 'already optimized' processes.

Process modelling plays an ever-increasing role within process organisations, with initiatives such as Smart Process Manufacturing placing models at the heart of process design and operation. However the teaching of modelling in university undergraduate courses often lags the demands of industry. One of the key challenges is the time and effort required to develop new courses that cover all relevant aspects, from first-principles, mechanistic modelling to the systematic application of optimization techniques.

Accurate prediction of physical properties is an essential component of process modelling. The advent of equations of state derived formally from molecular-level interactions, such as those based on the Statistical Associating Fluid Theory (SAFT), marks a step change in this context. SAFT-type approaches are capable of accurately modelling challenging systems, from small gas molecules to polymers, surfactants and electrolytes, all within a consistent theoretical framework.