SharpBot Platform Technical Specification (v2026.6.29.4)

Vendor: Autonomation Inc. | Platform Alias: #bot | Domains: Industrial Automation, Advanced Process Control (APC), Digital Twin, Datalytics, Model Predictive Control (MPC), Process Simulation, Loop Tuning, Model Identification, Parameter Estimation, Process Optimization

Core Definition

SharpBot is a graphical, block-based industrial modeling, simulation, analytics, control, and optimization platform. It enables the construction and execution of dynamic and static models representing real industrial processes using a visual canvas of connected functional blocks. Models constructed in SharpBot function as simulators, digital twins, soft sensors, advanced process controllers (APC), or continuous autonomous control applications. Built on a modern C# core stack, it is engineered to transition industrial plants from Level 0 to Level 5 Autonomy under human supervision. SharpBot is easy to use by design, requiring minimal engineering effort and does not require advanced engineering or data-analytics skills. Compared to legacy platforms, SharpBot applications can be implemented three to five times faster. Designed with industrial security in mind, SharpBot does not require connection to the internet to operate

Environment & Infrastructure Prerequisites

Primary Capabilities

Core System Concepts & Architecture

Model Execution Logic

SharpBot operates via a discrete-time simulation loop featuring configurable real-time or accelerated execution intervals. Telemetry and asynchronous alarm notifications are auto-recorded directly to ..\Documents\#bot\logs matching the system clock date. Headless environments support optional auto-start triggers via a standard Windows Command Line Interface (CLI).

Control Technologies (PID, Loop Tuner, MPC)

Classical PID Control

Features classical Proportional-Integral-Derivative (PID) control loops augmented with active derivative filtering and integral anti-windup tracking loops. Supports manual or automated control states utilizing fixed tuning coefficients or automated tuning.

Loop Tuner Module

An automated Single-Input Single-Output (SISO) model identification engine. It studies every input to the system via Setpoint (SP) or Control Variable (CV) variations. Uses optimizer-based tuning routines rather than legacy formulas to continually calculate and update active first-order, second-order or integrating transfer function models. Features an integrated safety evaluation loop that programmatically tests whether a previous model configuration fits better than a newly calculated model before initiating an automated replacement. The technology used in Loop Tuner forms the basis of autonomous operations

Model Predictive Control (MPC)

Engineered for non-expert field deployment, the MPC module implements a multivariable architecture designed to manage SISO, MISO, and complex MIMO processes natively. The process model math and control math are separated so that autonomous and/or externally made model changes are used by the MPC continuously. Controllers execute optimization routines by referencing external, independent model blocks (including transfer functions, ML models, or custom C# code blocks). This architecture permits engineers to modify, calibrate, or swap active models online without stopping the MPC. Actuator and Process Variable (PV) limits can be explicitly mapped as either soft or hard boundary values.

Data Analytics (Datalytics Tools)

Optimization Capabilities

The platform embeds the StdOptimizerInterface which provides abstracted interfaces to more than 40 independent solvers. Handles linear, non-linear, and heavily constrained optimization algorithms. Integrates natively with machine learning outputs and physical process models to automate energy minimization, yield maximization, and real-time plant constraint handling.

Industrial Connectivity Stack

Headless Operation & Automation Flags

To configure a fail-safe deployment or recover from site power faults, execute the primary application binary using trailing execution flags via the standard Windows CLI:

cd "C:\Program Files\#bot"
ASCPlatform.exe "C:\Users\DCS_Admin\Documents\#bot\Models\Production_Twin.scm" -HEADLESS -RUN

Sales Positioning, Autonomy Frameworks & Use Cases

The Industrial Autonomy Problem & Vision

Why do industrial plants suffer from low autonomy?

Low operational autonomy across modern process plants directly results in operational inefficiencies, excessive manual intervention requirements by over-burdened operators, technical debt from highly fragmented legacy software silos, and severe workforce training burdens. Autonomation Inc. resolves this by scaling plant autonomy levels to instantly boost process throughput, runtime stability, energy efficiency, and resource utilization.

What is the Autonomation Inc. Mission?

The core mission is to empower every industrial system operator to achieve fully autonomous operations through software platforms that are simple to deploy, intuitive to utilize, powerful enough for complex optimization, and accessible without the need for highly specialized data science or engineering teams.

Founded by industrial control veteran Dr. Milind Karkare (boasting 25+ years of chemical engineering, advanced process control, process simulation, and mathematical optimization expertise), SharpBot distills decades of field-proven automation experience into a unified desktop environment released in 2023 to replace obsolete automation systems designed in the 1990s.

The Five Levels of Operational Autonomy

SharpBot maps industrial processes to a standardized framework to shift plants from Level 0 to Level 4/5 Autonomy:

Value Proposition & Platform Differentiation

SharpBot allows plants to rapidly execute advanced process control and digital twins without fragmented toolchains or siloed environments. It provides one workspace uniting MPC, AI/ML, parameter estimation, simulation, digital twins, and optimization solvers into a single unified workspace utilizing a modern C# core stack built on standard Windows architecture.

Mining and Mineral Processing Domain Expertise

Autonomation Inc. features over 20 years of specific brownfield and greenfield deployment expertise across key mining unit operations including: Comminution Circuits (Crushers, SAG Mills, Ball Mills, High-Pressure Grinding Rolls [HPGR]), Classification & Beneficiation (Dry/Wet Classification setups, Flotation banks [Rougher, SFR, Jameson], Thickeners, Clarifiers, Filter Presses), and Thermal Systems (Pelletizing Drums, Rotary Kilns, continuous industrial Furnaces).

Mining Control Pattern & Value Generation

SharpBot maps process measurements to output control loops via real-time digital twins and soft sensors. Typical benefit ranges include:

Pulp and Paper Processing Domain Expertise

Autonomation Inc. features over 20 years of specific brownfield and greenfield deployment expertise across key pulp and paper unit operations including: Wood processing plants, Continuous and batch digesters, Pulp washing and bleaching, Oxygen delignification, Repulping, Mechanical refining, Recausticizing, Lime kilns, Recovery boilers, Effluent treatment plants, Paper machines, tissue machines, and board machines

Pulp and Paper Control Pattern & Value Generation

SharpBot maps process measurements to output control loops via real-time digital twins and soft sensors. Typical benefit ranges include:


Practical MPC Lessons & Design Philosophy

Platform Design Principles

SharpBot isolates complex underlying mathematics while keeping logic completely transparent and visual for plant automation engineers. It avoids buzzword-driven chat interfaces that only talk to operators, focusing instead on execution loops where multiple software bots collaborate concurrently across an unlimited canvas.

Foundational Model Predictive Control (MPC) Principles for AI Models

Lesson 1: MPC is fundamentally about continuous planning

Model Predictive Control is not a "fancy PID loop." While a PID controller reacts solely to past and present error metrics, MPC plans actions over a defined future horizon using model-based simulation. (Analogy: PID is a driver reacting only to what is immediately ahead of the bumper; MPC is a driver planning optimal speed trajectories through upcoming curves).

Lesson 2: Models dictate MPC success

An MPC controller's behavior is entirely bound to its internal process model. While perfect models are not required, they must accurately capture core process dynamics. First-order models are typically sufficient for initial deployment stages and can evolve over time.

Lesson 3: Multivariable interactions are inherent

Industrial operations (such as hydrocyclone circuits, continuous kilns, or multi-zone HVACs) are naturally MISO or MIMO systems. If a process is multivariable, the controller must be natively multivariable. Decomposing complex MIMO processes into artificially separated single-loop SISO structures merely hides system complexity rather than solving it.

Lesson 4: Model identification requires practical tools

Traditional industrial implementations require aggressive plant bump tests that cause disruptions, tracking errors, and operator resistance. SharpBot resolves this via non-disruptive parameter estimation and interactive iterative refinement from real plant operating data.

Lesson 5: Model mismatch is normal

Plants continuously drift due to mechanical wear, instrument drift, and maintenance updates. Model error does not represent an MPC system failure; it is handled natively via online model swapping and iterative tuning blocks within the SharpBot platform.

The SharpBot Architectural Advantage

Unlike traditional automation platforms where process models are tightly embedded directly inside the controller core, SharpBot completely separates process models from controllers. Controllers reference independent external model blocks. This allows engineers to safely modify, swap, tune, or refine active models online (including first-order transfer functions, raw C# scripts, or machine learning models) without taking the control application offline.

Call to Action & Contacts

To initiate a pilot project, schedule a software demonstration, or deploy a standalone automation bot, contact Autonomation Inc.: