SciTransfer
Organization

AVGI

Belgian SME applying AI and process simulation to optimize steam cracking and deliver CO2-neutral olefin manufacturing.

Technology SMEmanufacturingBESMEThin data (2/5)
H2020 projects
2
As coordinator
0
Total EC funding
€399K
Unique partners
23
What they do

Their core work

AVGI is a Belgian technology SME based in Zwijnaarde (Ghent's technology campus) specializing in computer-aided process design and simulation for the petrochemical industry, with a focus on steam cracking and olefin manufacturing. They develop and apply advanced process models to optimize industrial furnaces and chemical production processes, combining deep chemical engineering domain knowledge with computational methods. More recently, they have integrated AI and machine learning into process optimization workflows, targeting both efficiency gains and significant reduction of the CO2 footprint of ethylene production. Their work sits at the boundary between industrial process engineering and digital transformation of legacy chemical manufacturing.

Core expertise

What they specialise in

Process simulation and optimization for petrochemicalsprimary
2 projects

Both IMPROOF and OPTIMAL target optimization of steam cracking and olefin production processes using advanced modeling approaches.

Steam cracking furnace engineeringprimary
1 project

IMPROOF (2016-2020) was specifically focused on model-guided optimization of industrial steam cracking furnaces for ethylene production.

Computer-aided process design (CAPD)primary
1 project

OPTIMAL (2021-2026) lists computer-aided process design as a top keyword, indicating it is a core methodological competence.

AI and machine learning for process controlemerging
1 project

OPTIMAL applies artificial intelligence and machine learning to smart manufacturing and process control in olefin production.

Carbon capture and CO2 utilisation in industryemerging
1 project

OPTIMAL explicitly targets CO2-neutral olefin production, with carbon capture and CO2 utilisation listed as primary project keywords.

Evolution & trajectory

How they've shifted over time

Early focus
Steam cracking furnace optimization
Recent focus
AI-driven CO2-neutral olefin production

In their first H2020 project (IMPROOF, 2016-2020), AVGI's work centered on model-guided optimization of steam cracking furnaces — classical process systems engineering applied to one of the most energy-intensive operations in the chemical industry. No AI or sustainability keywords appear from that period, suggesting a primarily deterministic modeling and simulation capability. By their second project (OPTIMAL, 2021-2026), the profile shifted substantially: AI, machine learning, smart manufacturing, carbon capture, and CO2 utilisation all entered the picture alongside the continued process engineering foundation. The evolution follows a clear arc — from optimizing existing fossil-fuel processes to redesigning them as intelligent, low-carbon systems.

AVGI is moving from classical process modeling toward AI-augmented smart manufacturing with an explicit decarbonization mandate, positioning them well for the industrial green transition agenda that dominates current EU funding priorities.

Collaboration profile

How they like to work

Role: specialist_contributorReach: European9 countries collaborated

AVGI has participated in both of its projects as a contributor rather than coordinator, indicating they bring focused technical expertise rather than taking strategic or administrative leadership in consortia. With 23 distinct consortium partners from 9 countries across just 2 projects, they have consistently worked in large, internationally diverse teams — suggesting they are effective at integrating their specialized capabilities into complex multi-partner efforts. This profile is typical of a niche engineering SME that is sought out by consortium builders for specific process modeling or simulation capabilities.

Despite only two projects, AVGI has connected with 23 unique partners across 9 countries, reflecting participation in sizable, well-connected RIA and MSCA-RISE consortia. Their Ghent location places them adjacent to strong academic and industrial chemical engineering networks in Belgium and the broader Northwest European chemical corridor.

Why partner with them

What sets them apart

AVGI occupies a narrow but high-value niche: process systems engineering and AI-driven optimization specifically for steam cracking and olefin manufacturing, which are among the most carbon-intensive and economically significant processes in European industry. Very few SMEs combine this level of domain depth in ethylene production with active AI/ML integration and explicit CO2 neutrality goals — most players are either large industrial firms or academic groups. For consortia targeting industrial decarbonization or smart manufacturing in the chemical sector, AVGI offers a rare combination of applied process engineering know-how and emerging computational capabilities in a flexible SME format.

Notable projects

Highlights from their portfolio

  • IMPROOF
    AVGI's largest funded project (EUR 399,250 EC contribution), focused on integrated model-guided optimization of industrial steam cracking furnaces — a technically complex, high-impact domain in the petrochemical industry.
  • OPTIMAL
    A long-running RIA project (2021-2026) combining AI, machine learning, carbon capture, and CO2 utilisation to produce CO2-neutral olefins — representing AVGI's strategic pivot toward intelligent and sustainable chemical manufacturing.
Cross-sector capabilities
Energy transition and industrial decarbonizationDigital industry and AI for process controlClimate and environment — CO2 capture from industrial sources
Analysis note: Profile is based on only 2 projects. Keywords are available only for OPTIMAL; IMPROOF carries no keyword metadata in the dataset, so the early-period keyword field is empty and the early focus inference relies solely on the project title. The two projects tell a coherent story, but the thin data warrants caution — additional company information or deliverable data would significantly sharpen the profile.
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