EL4CHEM

Introduction

Chemical and manufacturing industries face increasing pressure to develop high-quality products faster, more sustainably, and at lower cost. However, translating laboratory insights into reliable industrial production remains complex and time-consuming, often requiring extensive experimentation and rework. At the same time, advanced digital technologies such as artificial intelligence and digital twins are not yet easily accessible to many domain experts. This challenge is highly relevant for sectors such as pharmaceuticals, consumer goods, specialty chemicals, and waste valorization, where efficiency, quality, and sustainability directly affect society and the economy. EL4CHEM addresses this gap by enabling smarter, data-efficient learning across the full development and production lifecycle.

Goal

The main goal of EL4CHEM is to enable faster, more reliable, and more sustainable development and operation of chemical and manufacturing processes. The project aims to create scalable and trustworthy digital models that can seamlessly evolve from laboratory to pilot and industrial scale. By making advanced modelling and AI tools easier to use, EL4CHEM empowers a broader group of engineers and operators to make better, data-driven decisions. Ultimately, the project seeks to reduce development time, improve product quality, and lower resource and energy consumption across multiple industrial domains.

Approach

EL4CHEM is carried out by a multidisciplinary consortium combining leading industrial companies and academic research partners. The industrial partners, Janssen Pharmaceutica NV, Procter & Gamble, Eastman, and Indaver, contribute real-life industrial cases from pharmaceuticals, consumer goods, specialty chemicals, and resource recovery, ensuring strong relevance and validation in operational environments. The research partners (KU Leuven CIPT, KU Leuven MeBioS, imec-UAntwerpen IDLab, and Flanders Make) develop the core methodologies in digital twins, artificial intelligence, process modelling, monitoring, and control. Together, the consortium co-develops scalable, data-efficient solutions that are tested and demonstrated on the industrial cases, with a strong focus on usability, collaboration, and transferability across sectors.

Expected impact and valorization

EL4CHEM is expected to deliver significant scientific, industrial, and societal impact. Industrial partners will benefit from improved process efficiency, reduced waste, enhanced product quality, and faster time-to-market. The project supports sustainability goals by lowering energy use, reducing material losses, and enabling circular economy practices such as resource recovery. Results will be valorized through deployment in industrial operations, licensing of developed methods, further collaborative research, and training of skilled professionals. In the longer term, EL4CHEM strengthens the innovation ecosystem by making advanced digital technologies a practical standard in chemical and manufacturing industries.