Process industries are characterized by intense use of raw resources and energy, thus providing a context where even small optimizations can lead to savings both in terms of economic and environmental costs. This is especially true for specific industrial processes such as aluminium smelting or injection moulding, characterized by production in high volumes divided among distributed production units, across several lines, plants or even sites.
Predictive modelling techniques can be especially effective in optimizing processes in such context, but their application is not straightforward for several reasons including e.g. the high cost of integrating new sensors or actuators into legacy production, difficulties in monitoring physical parameters in harsh conditions, interoperability issues, difficulties in application fusing and correlating information collected at different SCADA levels, challenges in defining KPIs, etc. As a consequence, the deployment of model-based predictive functions in such production environment at a sustainable cost or with sufficient reliability is not always feasible, resulting in optimization potentials remaining untapped.
In past markets characterized by lower international competition, stable demand, relatively low labour cost and high abundance of raw materials, industry was able to remain viable just through progressive improvements in production technology, organization and logistics. The change in global competition and resources availability calls instead for a drastic re-invention and re-design of production processes and sites. Enabling benefits by integrating innovations in the installed process base is a fundamental step to help process industries transitioning from the current model oriented to the production of goods by consuming resources, to newer “circular” models. In this perspective, resource, cost and environmental sustainability is considered, monitored and optimized at all times, resulting in benefits for industries and society as a whole.
MONSOON project aims at establishing data-driven methodology and tools to support identification and exploitation of optimization potentials through model based predictive controls. The data lab enables multidisciplinary teams to jointly model, develop, simulate, verify, deploy and evaluate distributed predictions and controls. This will help plants in meeting their optimization.
MONSOON achieved to develop a flexible and scalable infrastructure able to support a model-based development environment dedicated to easier the study, the creation and the test of function dedicated to improve and optimize processes. MONSOON solution has been tested by data scientists and end users in order to better align the finalization of the requirements. In fact, data scientists created new or improved existing predictive functions, regularly cooperating with the domain experts. The predictive functions for the different scenarios have been created and tested by using MONSOON platform. Nevertheless, the consortium faced and overcame several technical and not technical challenges related to the production and plant needs and schedule. All the challenges gave to the consortium many important lessons learned.
