Epicor Connected Process Control
Epicor Connected Process Control provides a simple-to-use software solution that allows you to configure digital work instructions and enforce process control. It also ensures that operations are error-proof. Connect IoT devices to collect 100% time studies and process data, images and images at the task level. Real-time visibility and quality control on a new level! eFlex can handle any number of product variations or thousands of parts, whether you are a component-based or model-based manufacturer. Work instructions can be linked to Bill of Materials, ensuring that products are built correctly every time, even if changes are made during the process. Work instructions that are part a system that is advanced will automatically react to model and component variations and only display the right work instructions for what's currently being built at station.
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Control D
Control D is a customizable DNS filtering and traffic redirection platform that leverages Secure DNS protocols like DNS-over-HTTPS, DNS-over-TLS and DNS-over-QUIC, with support for Legacy DNS.
With Control D you can: block malicious threats, block unwanted types of content network wide (ads & trackers, IoT telemetry, adult content, socials, and more), redirect traffic using transparent proxies and gain visibility on network events and usage patterns, with client level granularity.
Think of it as your personal Authoritative DNS resolver for the entire Internet that gives you granular control over what domains get resolved, redirected or blocked.
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Cybernetica CENIT
Cybernetica specializes in providing Nonlinear Model Predictive Control (NMPC) utilizing mechanistic models. Our innovative software solution, Cybernetica CENIT, features a versatile architecture capable of addressing diverse industrial challenges by delivering optimal strategies. This includes advanced multivariable optimal control, predictive control mechanisms, and intelligent feed-forward strategies, along with efficient handling of constraints. Furthermore, our adaptive control capabilities leverage state and parameter estimation, incorporating feedback from indirect measurements via the process model. The use of nonlinear models allows for effective operation across extensive ranges, enhancing the management of nonlinear processes. This leads to a diminished reliance on step-response experiments and bolstered accuracy in state and parameter estimations. Additionally, we offer control solutions for both batch and semi-batch operations, efficiently managing nonlinear processes that function under fluctuating conditions. Our technology also ensures optimal grade transitions in continuous operations, safe supervision of exothermic processes, and control of unmeasured variables, including conversion rates and product quality. As a result, we contribute to reduced energy consumption and a lower carbon footprint, while also enhancing overall process efficiency. In summary, Cybernetica is committed to advancing industrial control solutions that optimize performance and sustainability.
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COLUMBO
A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.
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