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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Cybernetica CENIT
Cybernetica delivers Nonlinear Model Predictive Control (NMPC) based on mechanistic models. Our software product, Cybernetica CENIT, offers a flexible architecture that can meet any industrial challenge with optimal solutions. Multivariable optimal control, predictive control, intelligent feed forward, optimal constraint handling. Adaptive control through state and parameter estimation, and feedback from indirect measurements through the process model. Nonlinear models are valid over larger operating ranges. Improved control of nonlinear processes. Less need for step-response experiments and improved state and parameter estimates. Control of batch and semi-batch processes, control of nonlinear processes operated under varying conditions. Optimal grade transition in continuous processes. Safe control of exothermal processes and control of unmeasured variables, such as conversion rates and product quality. Reduced energy consumption and carbon footprint.
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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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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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