Solutions
Simulation-as-a-Service
Process modeling and simulation enables industries to plan new processes or test changes to existing ones without cost or risk to the business. It assists them from the early stage of conceptual design to scale-up, technology transfer, post processing, and rejuvenation. The mechanistic modeling, or digital twins, creates predictive tools to enable knowledge-based decision making. By comparing different scenarios and case studies, companies can consider all possible angles (via sensitivity analysis) and make confident decisions. With simulations, you can run months or years into the future in seconds, get answers now and plan for different scenarios before it’s too late to react, saving you time, resources, and cost in the long-term.
Procegence provides on-demand access to modeling expertise and various [appropriate] software platforms with diverse and fit-for-purpose solutions. Our service model is flexible and is customized to meet the specific needs of each client, with continuous support along the project life cycle. We believe in a co-creation culture and also provide on-site support for model development, validation, and implementation. The comprehensive long-term “strategic partnership” or “project based” collaborations are designed to serve different size clients and projects.
Digital Transformation
Digital transformation provides the means to unlock a new level of productivity enhancement. The manufacturing operations present one of the biggest and most readily accessible areas of opportunity. Most chemical plants continuously generate an enormous amount of data, but discard most of it. Managers can collect the data and interpret it to reveal ways to achieve higher yields and throughput, lower energy/material consumption, and more effective maintenance. This can be easily achieved by using existing IT, process/product knowledge, and process control systems.
Procegence helps clients with collecting and transforming process data to information and then generating knowledge from this information to optimize the process and generate value. The digital transformation can be utilized horizontally and vertically in the enterprise for scheduling, predictive maintenance, supply chain reliability, fault detection, pattern recognition, and advanced process control. The digital tools and digital twin of the process can be deployed on-site or via cloud to consolidate data in application platforms and data dashboards for visualization, data historian, and future trend prediction.
Optimization
Process Optimization is one of the main objectives in the process design, operation, and to the enterprise level. An optimized process is more robust, runs at a faster speed, produces a higher yield, and consumes less resources. Many of the unit operations and processes are not designed to run at optimum conditions, incorporating an arbitrary design margin, or due to reutilization of a plant or change of product, the process became out of optimum point. Although a DoE based design space provides a safe working domain, a global or local optimization can improve the process productivity, quality, robustness, and efficiency to a great extent.
Procegence helps clients with defining the baseline of their process, evaluating the equipment and process line, running multidimensional mathematical models (data-driven, mechanistic, or hybrid), and offering customized optimization solutions. The optimization studies are developed based on the objectives of need and techno economical evaluation.