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A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering

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Performance Evaluation Methodologies and Tools (VALUETOOLS 2022)


Processes in chemical engineering are frequently enacted by one-of-a-kind devices that implement dynamic processes with feedback regulations designed according to experimental studies and empirical tuning of new devices after the experience obtained on similar setups. While application of artificial intelligence based solutions is largely advocated by researchers in several fields of chemical engineering to face the problems deriving from these practices, few actual cases exist in literature and in industrial plants that leverage currently available tools as much as other application fields suggest. One of the factors that is limiting the spread of AI-based solutions in the field is the lack of tools that support the evaluation of the needs of plants, be those existing or to-be settlements. In this paper we provide a Domain Specific Language based approach for the evaluation of the basic performance requirements for cloud-based setups capable of supporting chemical engineering plants, with a metaphor that attempts to bridge the two worlds.

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    The ECU, EC2 Computing Unit, is a measure defined by Amazon to compare computing power of a VM with respect to its reference VM on Amazon AWS cloud, that can be considered as equivalent to a 1.0–1.2 GHz 2007 Intel Xeon or AMD Opteron CPU; it is now less popular than when it was introduced, but we consider it fit to the context and the purpose of this paper.


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This work has been partially funded by the internal competitive funding program “VALERE: VAnviteLli pEr la RicErca” of Università degli Studi della Campania “Luigi Vanvitelli” and is part of the research activity developed within Industrial Ph.D. Programme PON 2014-2020.

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Correspondence to Mauro Iacono .

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Campanile, L., Di Bonito, L.P., Gribaudo, M., Iacono, M. (2023). A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering. In: Hyytiä, E., Kavitha, V. (eds) Performance Evaluation Methodologies and Tools. VALUETOOLS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 482. Springer, Cham.

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  • Print ISBN: 978-3-031-31233-5

  • Online ISBN: 978-3-031-31234-2

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