New Project - ARCO-CHAIN

Project Summary:

Crystallization is a widely used technology for producing a particulate product with a defined particle size and distribution (PSD), crystal solid form and purity. In the pharmaceutical and fine chemical industry, batch operation is currently still the standard process for industrial crystallization, especially for small production capacities. There are a large number of products in this category as, for example, more than 90 % of active pharmaceutical ingredients and numerous fine chemicals are crystalline organic compounds with a low production rate of 250 to 1000 kg/a. Conventional batch processes are characterized by high variability, which can lead to an inconsistent product, making the processes inefficient and costly. Continuous crystallization processes, such as slug-flow crystallization, are promising in terms of lower investment and operating costs, better control and higher flexibility, but face major challenges in terms of modeling, sensing, actuation and efficient and adaptive control strategies. In the first phase of the project, we addressed these challenges by equipping a slug flow crystallizer (SFC) with sensors and actuators that enable online control of a defined PSD. We developed static and dynamic models and formulated robust model predictive control approaches to achieve autonomous operation of the SFC. In the second phase of the project, we will extend the autonomous operation to the full continuous crystal process chain (CPC) by connecting the SFC to the continuous vacuum screw filter (CVSF). The CVSF combines filtration, washing and drying in a single apparatus, resulting in a complex process chain. The central goal of ARCO-CHAIN is to achieve autonomous process control of a fully continuous CPC in order to achieve high-quality products with a minimum of resources and energy and maximum product yield. To achieve this within the project, the following objectives will be pursued: (1) integration of sensors and actuators that enable control of the CVSF, (2) development of detailed models for the CVSF with links to transfer the individual outputs from one unit to the next (including the SFC), and surrogate models that ensure basic physical balances, (3) development of a model predictive control algorithm that explicitly accounts for uncertainties and links between basic operations, and (4) demonstration of autonomous operation of continuous CPC. The main outcome of ARCO-CHAIN will be an adaptive and robust process control strategy for continuous CPC in a laboratory plant that is able to cope with uncertainties and disturbances during autonomous operation.