During ADIPEC 2025 we have introduced our approach for AI integration which (based on our proprietary database) now applying to enhance the Arvox SRT solutions, streamline remediation strategy and provide accurate environmental impact assessment.
Today, artificial intelligence mechanisms are being actively integrated across markets, influencing nearly all industrial processes—from planning to direct process management. However, one of the major challenges in this transformation is the universality of AI task delegation. It is important to recognise that the term “AI” encompasses a wide range of solutions, none of which serve as a silver bullet for all problems or can effectively manage every specific domain.
For example, large language models (LLMs) demonstrate excellent communicative abilities and perform remarkably well in knowledge compilation tasks. Yet, they have shown limited capability in generating new knowledge, often exhibiting so-called hallucinations—producing nonexistent or erroneous information to satisfy a user’s query as interpreted by the algorithm.
The current enthusiasm around universal LLMs has led to their rapid advancement, but it does not guarantee the reliability of their outputs nor replace the need for competent task formulation in industry. To achieve rational AI integration, particularly in the environmental technology sector, it is crucial to carefully define which data are suitable for training neural networks and to rely on goal-oriented AI models rather than universal ones.
When working with proprietary data, the most popular datasets are often those with the lowest acquisition cost—currently, this primarily includes geospatial data obtained through remote sensing. However, this also highlights the exceptional value of proprietary datasets generated by traditional chemical analytics, which are costly and time-consuming but highly precise.
Over more than a decade, Arva Greentech Remediation AG has accumulated a unique dataset produced using the industry’s leading analytical technique for pollution assessment—gas chromatography–mass spectrometry (GC–MS)—along with results from numerous field tests and pilot projects across the Middle East and parts of Europe. The integration of this dataset with modern geo-analytical data forms the basis of artificial intelligence algorithms, which we began successfully testing and applying this year in the development of environmental protection solutions.
We believe that such data-driven, reliable AI systems represent the future of rational decision-making in environmental technologies—where ecosystem restoration goals are achieved with minimal environmental footprint and maximum sustainability and resilience through the optimisation of technological solutions across all three pillars of sustainability.
