Machine
learning
that makes process control simpler
Conjecture System Identification Simple, best-fit models of your industrial systems for smoother process control
What if process control for industrial systems could be easier?
Conjecture System
Identification uses machine learning techniques on industrial data to build models that
reflect
real-world processes - in a simple, intuitive way. These models can be used to improve
control systems, reduce energy consumption, and improve the lifespan of existing instrumentation.
Conjecture modelling can also aid in
automating instrument maintenance, allowing for early detection
of faults and maximising operation time.
Conjecture System
Identification can be used in any industry which requires automated feedback loop
control
of processes, including oil and gas, minerals, and
manufacturing.
The Benefits
Better energy efficiency
Save on costs with enhanced automated control and select optimal operating modes
Improved reliability
Reduce moving parts and wear-and-tear, and stabilise your plant with amazing control
Behavioural diagnostics
Detect plant malfunctions early on by comparing model and underlying process
The Software
Conjecture System
Identification is a software solution that models dynamic industrial processes by fitting
best-fit trends to process data (such as SCADA data). The resulting model can aid engineers
in
enhancing
the stability of their plant controller, reducing movement and wear-and-tear in their
equipment, and improving the cost-efficiency of their system.
Conjecture System
Identification is easy to learn and use with either the desktop application or our online
portal (beta version).
User tutorial videos are
accessible from our Resources page, and email support is available.
User-friendly program
interface
Enter and manipulate all your process dynamics in one place
Include delays and output shifts for estimation thanks to recent machine learning algorithms that run in the background
See your model equations take shape as you build - equations are in the time domain and easily understood
Specify initial guesses for your model estimates, or use Conjecture's defaults
See the results of your calculations in the Results pane - including estimated gain
Built in Plot Model
function
View your input, output, and model on the same plot
Zoom in and out to visualise how well your model fits the outputs from your process data
Watch how modelling cuts through the noise in your data, leaving the key patterns for observation
Stored results to help
you
choose the most useful model
Conjecture stores every model you build during a session
Each model is stored with its results and ranked by goodness of fit (measured using the mean squared error)
Use this information along with your visualised plots to select the most useful model to characterise your system
