Contiamo was engaged by the project partner after they had successfully developed a technical solution to optimize the cleaning process: by measuring the water pollution during different phases of the cleaning cycle. For each cleaning observation, the data generated consisted of a collection of time series generated by a variety of sensors (flow, pressure, temperature, etc.).
In the first phase of the project, exploratory analytics were performed and trial models were created and presented to the client.
Subsequently, the prediction target was concretely defined. Algorithms were used to generate and test features like turbidity metrics or flow measurements. Based on this, the most promising prediction models were selected.
After the initial training, we realized that a more streamlined and powerful process was required to train and maintain the artificial intelligence. We create a new environment to leverage NVIDA GPUs which significantly reduced the model training time.
After successfully demonstrating the technical feasibility of process optimization, the Contiamo team supported the customer in evaluating the further (organizational and technological) requirements for a real-world implementation. This included setting up the technical infrastructure (real-time sensors) and the data infrastructure as well as an analysis of the return on investment.