MPCPy
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.
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COLUMBO
Closed-loop universal multivariable optimizer for Model Predictive Control (MPC) performance and Model Predictive Control (MPC) quality improvements. Use data in Excel files from DMC (Dynamic Matrix Control) from Aspen Tech, or from RMPCT (Robust Model Predictive Control Technology) from Honeywell, or Predict Pro from Emerson and use it to generate and improve correct models for the various MV-CV pairs. Amazing new optimization technology does not need step tests as required by Aspen tech, Honeywell, and others. It Works entirely in the time domain, is easy to use, compact, and practical. Model Predictive Controls (MPC) can have 10s or 100s of dynamic models. One or more could be wrong. Bad (wrong) Model Predictive Control (MPC) dynamic models produce a bias (model prediction error) between the predicted signal and the measured signal coming from the sensor. COLUMBO will help you to improve Model Predictive Control (MPC) models with either open-loop or completely closed-loop data.
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Cruz Operations Center (CruzOC)
CruzOC is a scalable multi-vendor network management and IT operations tool for robust yet easy-to-use netops. Key features of CruzOC’s integrated and automated management include performance monitoring, configuration management, and lifecycle management for 1000s of vendors and converging technologies. With CruzOC, administrators have implicit automation to control their data center operations and critical resources, improve network and service quality, accelerate network and service deployments, and lower operating costs. The result is comprehensive and automated problem resolution from a single-pane-of-glass. Cruz Monitoring & Management. NMS, monitoring & analytics -- health, NPM, traffic, log, change. Automation & configuration management -- compliance, security, orchestration, provisioning, patch, update, configuration, access control. Automated deployment -- auto-deploy, ZTP, remote deploy. Deployments available on-premise and from the cloud.
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Aspen DMC3
Develop more accurate and sustainable APC models covering a wider operational range by combining linear and nonlinear variables with deep learning. Improve ROI with rapid controller deployment, continuous model improvement and simplified workflows to enable easier use by engineers. Revolutionize model building with AI and streamline controller tuning with step-by-step wizards to specify linear and nonlinear optimization objectives. Increase controller uptime by accessing, visualizing and analyzing real-time KPIs in the cloud. In today’s ever-evolving global economy, energy and chemical companies need to operate with newfound agility to meet market demand and maximize margins. Aspen DMC3 is a next-generation digital technology helping companies sustain a 2-5% improvement in throughput, a 3% increase in yield and 10% reduction in energy consumption. Learn more about next-generation advanced process control technology.
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