The Optimization page is comprised of sections dedicated to a plant’s specific topology and technology. Optimizations are built on algorithms that use all data in the platform to project more optimal setpoints compared to where the operation is currently.

There are two main tools in the Optimization feature: Playbook and Plant-Specific Optimizations (PSOs). Playbook allows users to configure Studies that perform multi-variable, multi-outcome analysis on historical plant data to identify points in time where the plant ran more efficiently. Plant-Specific Optimizations are Pani-defined optimizations based on a combination of historical data and Digital Twin utilization. They are typically far more complex than Playbook studies and can deliver some of the most detailed and precise recommendations for optimization on the market today.

 

Both Playbook and Plant-Specific Optimizations, like Reverse Osmosis, rely on the concept of Studies. Studies are collections of assumptions, measurements, constraints, and variables that are used to perform the optimization calculations.

 

Playbook is a powerful multi-variable, multi-outcome decision engine that provides process analysts and operators an easy-to-use tool for deep analysis of plant performance.

Introduction to Playbook

Playbook studies search through historical records and provides optimum set-points based on a single objective or multi-objective search. Playbook is always monitoring current plant data and comparing it to the plant's historical data. If it finds matching results in the historical data, it presents and ranks numerous recommendations when the data and setpoints resulted in better plant performance.

 

Playbook creates Studies that define parameters and outcomes that the system will attempt to solve for and presents these to the user.

Playbook recommendations rely on the concepts of Similarity and Benefit, and the balance between these. The balance between these is set using the Minimum Similarity parameter in the Playbook Study Wizard.

Similarity refers to a point in the past where the operations and study parameters reflect the current plant operations. It assumes things like no major equipment changes have occurred, that membrane performance is similar based on its calculations. Similarity does consider significant operational anomalies and identifies things like membrane cleaning and replacement to help define similarity.

 

Benefit refers to the optimization of the outcome that maximizes the goal of the study. So, if the study was designed to save energy and the study was weighted for less Similarity (more Benefit), the recommended study might show higher energy savings, but the past operating conditions of the plant may not be an exact match to the plant today.

 

Playbook can be set to re-compute the study at prescribed Intervals. The important feature means that the Playbook can constantly review plant operations and can generate Benefits and operating guidelines autonomously.

Playbook gives the operations team confidence that they are running at optimal levels (relative to historical events) for water recovery, energy consumption, cleaning procedures, and more.

 

Plant-Specific Optimizations (PSOs)

PSOs are detailed studies developed by Pani Energy and focused on specific plant topologies and technologies, such as Reverse Osmosis, UF, wastewater systems, etc. The goal of the PSOs is to use historical data, AI, and simulation to optimize setpoints and plant operations.