Active Ingredient Manufacturing (AIM) processes are common production platforms for many formulated products in the specialty chemicals and agrochemicals industries. The common unit operations include fluid-fluid separation, reaction, crystallization, filtration, and isolation. Process engineers, scientists, and technical management teams all face significant challenges due to the complexity of these individual and combined unit operations within their production processes, with large multi-dimensional decision space and constricted timelines being the norm.

Science-based data-calibrated digital twins based on mechanistic models are powerful solutions to solve these challenges. It combines process understanding with targeted experimentation to describe and predict quantitative behaviour of critical attributes for process design, optimisation, risk assessment, design space exploration and scale up/technology transfer. As a result, it helps in achieving key business objectives such as meeting the desired product quality, reducing the cost of experimentation, accelerating the time to market, etc.

gPROMS FormulatedProducts & gPROMS Process provide a broad offering of these science-based data-calibrated digital twins based on mechanistic models. The models cover the full spectrum of common unit operations found in the specialty chemicals and agrochemicals industries. These models can be used for bringing value to the businesses across R&D, engineering, and manufacturing functions. Some of the applications include - predicting the effect of the batch recipe on the particle size & shape distribution, monitoring impurity formation, and tracking it across manufacturing operations and describing the formation of undesired solid phase(s).

Examples of common challenges and questions we aim to address using digital twins include:

  • I am working on scale-up, technology transfer and optimisation. How can data-calibrated digital twins speed up process development whilst providing a more fundamental understanding of the behaviour of a process?
  • My organization is interested in continuous production to increase productivity – how can modelling help to identify if batch/continuous operation is more optimal?
  • How can digital twins be deployed online to bring value in manufacturing?
  • My organization has no/very little prior experience in mechanistic modelling. How can we harness the benefits of science-based digital twins without prior experience?

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.

What this webinar covers

  • An overview of industrial challenges and Siemens PSE’s vision on how model-based solutions can support addressing them.
  • Overview of typical mechanistic model application workflows for model validation & application to support scale-up & technology transfer activities
  • Industrial case studies demonstrating applications where model-based solutions for AIM unit operations and their methodologies can provide value, including:
    • Enabling increases in R&D efficiency in AIM process design and optimisation
    • De-risking the technology transfer and scale-up of AIM processes

Who should attend?

Those with an interest in AIM processes from the specialty chemicals & agrochemicals industries, although this webinar may also be of interest to others in pharma – synthetics and food & beverage industries, including:

  • Scientists, process & control engineers, and expert process modellers alike in technical roles in R&D, engineering, and operations.
  • Decision-makers with an interest in learning about how AIM digital twins can bring value to their businesses across R&D, engineering, and manufacturing functions.


Dr Niall Mitchell, Principal Consultant and Strategy Director
Dr Niall Mitchell, Principal Consultant and Strategy Director, Siemens Process Systems Engineering (SPSE)

Dr Niall Mitchell is a Principal Consultant and Strategy Director for Animal Health, Specialty & Agrochemicals at SPSE. He is a Chemical Engineer with a PhD on the mechanistic modelling and experimental aspects of crystallization from the University of Limerick. Niall has over ten years of industrial experience in reaction & fluid separations, crystallization, size reduction & solid processing and is responsible for the development & application of PSE’s tools in the area of Active Ingredient Manufacture.

More Information


24 February 2022, 10:00 EST/ 15:00 GMT


45 minutes plus Q&A


There is an error on the x-axis label on the x-y scatter plot shown on slide 28 of the webinar. This should have read as "Seed location parameter (µm)"" and not as "Seed mass".