Research Article
A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering
@INPROCEEDINGS{10.1007/978-3-031-31234-2_8, author={Lelio Campanile and Luigi Piero Di Bonito and Marco Gribaudo and Mauro Iacono}, title={A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering}, proceedings={Performance Evaluation Methodologies and Tools. 15th EAI International Conference, VALUETOOLS 2022, Virtual Event, November 2022, Proceedings}, proceedings_a={VALUETOOLS}, year={2023}, month={5}, keywords={Artificial intelligence process engineering domain specific language performance evaluation cloud computing}, doi={10.1007/978-3-031-31234-2_8} }- Lelio Campanile
Luigi Piero Di Bonito
Marco Gribaudo
Mauro Iacono
Year: 2023
A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering
VALUETOOLS
Springer
DOI: 10.1007/978-3-031-31234-2_8
Abstract
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.
