Guides · documentation · Q&A
2 guides · 15 documents · 9 answers
Resources
How Acaysia connects to your PLC, how it fails safe, and what it does for yield and energy.
-
Start here Yield · energy · throughput
What is advanced process control?
Start here if APC is new to you: what it is, what it does for yield, energy, and throughput, and where MPPI fits in.
acaysia.com/resources/advanced-process-control Read the guide -
Comparison PID · MPC · MPPI
MPPI vs PID vs MPC
How Model Predictive Path Integral control compares to PID and classical MPC, and why sampling handles the nonlinear chemical reactor dynamics that linearized solvers struggle with.
acaysia.com/resources/mppi-vs-pid-mpc Read the guide
Documentation
15 documents · by access code
Documentation, case studies and whitepapers
These contain patent-pending technology and are available by access code only. Enter your code once and every document opens for this session.
No code yet? Request one from founders@acaysia.com or through the contact form.
Technical documentation
5 documents
- Integration PLC Integration Guide: OPC UA Step-by-step guide for connecting Acaysia to your existing PLC infrastructure via OPC UA and EtherNet/IP protocols. Requires access code
- Control theory MPPI Control: Theory and Practice Deep dive into Model Predictive Path Integral control algorithms and their application to physical process optimization. Requires access code
- Safety Safety Architecture Overview ASIL-D inspired safety design, SIL compatibility, and multi-layer failsafe architecture documentation. Requires access code
- Deployment Deployment Checklist Complete pre-deployment, installation, and validation checklist for Shadow Mode through Closed-Loop Control. Requires access code
- API reference API Documentation Full REST API reference for the Acaysia control system, including historian access and configuration endpoints. Requires access code
Case studies
3 modeled examples
- Modeled Example Specialty Chemicals: 1.8% Projected Yield Improvement A modeled specialty-chemicals reaction, and the yield MPPI control recovers from it. Requires access code
- Modeled Example Petrochemical Refinery: Projected Energy Reduction What adaptive MPPI control does to energy consumption in a modeled refinery operation. Requires access code
- Modeled Example Pharmaceutical: Projected Batch Consistency How physics-informed ML tightens batch-to-batch consistency in a modeled pharma process. Requires access code
Whitepapers
4 papers
- Modeling Gray-Box vs Black-Box Models in Process Control Comparing physics-informed and pure data-driven approaches to chemical reactor modeling. Requires access code
- Economics The Economics of 1% Yield Improvements Quantifying the financial impact of small yield improvements at scale in chemical manufacturing. Requires access code
- Data Federated Learning for Chemical Processes How federated learning enables cross-plant model improvement without sharing proprietary process data. Requires access code
- Cybersecurity ISA/IEC 62443 Compliance Guide Meeting industrial cybersecurity standards for AI-powered control systems in chemical manufacturing. Requires access code
Blog and updates
3 posts
- Company Introducing Acaysia Our founding story and the vision for intelligent physical process control. Requires access code
- Adaptive control Why Chemical Plants Need Adaptive Control The case for moving beyond fixed PID parameters to ML-guided adaptive control. Requires access code
- Integration Drop-In PLC Integration via OPC UA Technical deep-dive into how Acaysia connects to industrial PLCs without disruption. Requires access code
Need a code, or a document not listed? founders@acaysia.com
Frequently asked questions
Integration · deployment · safety · return
Frequently asked questions
What does Acaysia do?
Acaysia is a control system that optimizes physical processes for yield, energy, and throughput. It models each unit operation as a typed object and computes control moves in real time. It is validated on our reactor platform and a six-PLC test bench, and built for any process, in any plant.
Why does Acaysia work across different processes?
Acaysia runs on a typed compositional ontology of unit operations. Each process is described by conserved quantities, ports, state variables, and manipulated variables. The controller reads the ontology rather than hard-coding one process, so the same architecture extends from reactors to columns to crystallizers.
What types of unit operations does Acaysia support?
Acaysia runs on continuous stirred-tank reactors today. The same architecture extends to batch reactors, plug flow reactors, distillation columns, crystallizers, and evaporators. Chemical manufacturing is the first vertical.
How does MPPI control differ from traditional PID control?
PID reacts to the error it sees right now. MPPI (Model Predictive Path Integral) control couples machine learning with predictive optimization to anticipate where the reactor is heading and pick the best move before a disturbance lands. The payoff is higher yield, lower energy use, and faster recovery from upsets, with PID still underneath as the safety net.
Is Acaysia compatible with existing PLC systems like Rockwell and Siemens?
Yes. Acaysia connects to existing PLCs from Rockwell, Siemens, and other major vendors over the standard protocols, OPC UA and EtherNet/IP. No rip-and-replace; it drops in alongside what you already run.
What is Shadow Mode and how does deployment work?
Shadow Mode is the first phase of deployment where Acaysia observes your reactor operations without making control decisions. A learned residual tracks fouling and drift. The system then progresses through Advisory Mode (recommendations only) before optional Closed-Loop Control with full automation.
How quickly can the failsafe system respond to issues?
Acaysia’s failsafe responds in under 100 milliseconds, automatically reverting to proven PID control or a safe shutdown when it detects an anomaly.
Is it safe to run in a real plant?
Yes. A Trust Arbiter checks every control move against plant limits, and the system falls back to existing PID control in under 100 milliseconds on any fault. The safety architecture is ASIL-D inspired and SIL compatible and never interferes with Safety Instrumented Systems. Shadow Mode is read-only.
What kind of ROI can we expect from implementing Acaysia?
An estimated 2% yield improvement and around 10% energy reduction; results vary by customer and process. Every deployment starts in Shadow Mode for 30 days, where you compare Acaysia’s predictions against your PID’s actual performance before it touches control. Detailed economics are available under NDA.