Contamination problems are difficult to solve. The data are typically limited and uncertain. The remediation processes are complex and expensive. The stakes are high. The consequences of failure are severe.
Our commercial product ChemML applies ML to analyze and predict contaminantion processes.
ChemML is not a black-box. It employs novel science-informed ML models that are robust, trustworthy, and defensible. It facilitates the management and remediation of contaminated sites.
It is important to note that traditional models representing complex geochemical process accounting for multiple geochemical species are challenging to develop. Typically, these models also requires huge computational resources and execution times. As a result, the assimilation of all the available site data in these models is challenging and, in many cases, impossible.
The traditional models also are typically underestimation contamination impacts and overestimating effects on the remdeication activities. This is caused by the way governing processes are represented in the models. As a result, the model predictions can be erroneous and derail projects from success.
ChemML overcomes these limitations.
ChemML is designed for deployment as a Software-as-a-Service (SaaS) on the cloud. Tiered licensing and customer support options are available. APIs to public and proprietary datasets can be provided to address customer needs. The software frontend allows for data management and visualization tools. The backend provides access to cloud-computing resources for AI/ML analyses and interpretation. The commercial product includes tools for sensitivity, uncertainty, and decision analyses. Access to actionable information can be delivered in near-real-time to the users through IoT devices and phone apps.
Please contact us for licensing information, commercial support, and consulting.
