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Course Outline
Session 1
- Intro – Review objectives, methods, define AI, digital tools as target of course, assignments, “grading”
- Users – Managers, engineers, supervisors, field staff, public. Labor replacement or supplement?
Session 2:
- Tech basics – Data science models, AI regressions, ML, agents. Bespoke or modular?
- Tech basics – What is “good” data, data cleaning methods, instrument monitoring
Session 3:
- Identifying good use cases #1 – Case study discussion chem optimization
- Identifying good use cases #2 – Case study discussion collection system predictions
Session 4:
- Tech basics – Digital twins, mechanistic and hybrid models
- Identifying good use cases #3 – Case study discussion hybrid optimizer WW simulator
Session 5:
- Tech basics – LLMs and Computer Vision
- Tech basics – Platforms: aggregators, modules, widgets, portability, security, scaling, support models
Session 6:
- Tech basics – Data storage: SCADA Historian, databases, transfers, APIs, data management
- Tech basics - Data connectors: cybersec, air gaps, data diodes, read only and write back, on-prem vs. cloud, coord with IT/OT
Session 7:
- Deployment philosophy – Direct control or advisory; push notices or passive dashboards; utilization tracking
- Developers and products – Consultants, academia, equipment vendors, niche, digital platforms, utility DIY
Session 8:
- Digital governance – AI policy, data access and retention vs. transparency, ad-hoc or planned, organizational alignment, etc.
- Change management – communication plans, rollout, champions, management support, pre-mortem, pilots and scaling, monitoring, feedback loops, ongoing tech support
Session 9:
- Commercial models – What is the value? ROI, SaaS, consulting, capital expense, procurement issues
- Legal considerations – Intellectual property, model ownership, data ownership, NDAs, current AI laws in different states, personal data management and restrictions
Session 10:
- Research topics on AI in water sector, future direction
- Wrap up and summary discussion