Conference Paper

Maximizing Efficiency and Transparency in Batch Processing with Simulation-driven Predictive Decision Support

Authors:
  • INOSIM Software GmbH / INOSIM Consulting GmbH
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

The complexity of modern industrial batch plants makes it nearly impossible for plant personnel and production managers to predict their future behavior accurately over longer periods of time. This contribution presents the INOSIM Foresight system for predictive decision support that employs highly accurate dynamic material flow simulation to create a transparency and situational awareness that allows the plant operators and management to operate and plan more efficiently, productively, and safely, which can lead to savings in the millions. Intuitive predictive graphical dashboards support plant personnel in a variety of ways. For example, they provide concise guidance to improve production (e.g. by counteracting negative events before they occur and by predicting key KPIs days or even weeks ahead), and they enable the staff to create production, personnel, and in-process maintenance plans that are efficient and non-conservative. The system is currently being deployed at a new, innovative pharma batch plant of CSL Behring GmbH in Marburg, Germany.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Data visualization is crucial in today’s data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization.
Chapter
Full-text available
A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches.
Proposal of a European Research and Innovation Agenda on Cyber-physical Systems of Systems
  • T U Dortmund
S. Engell and C. Sonntag, Eds., Proposal of a European Research and Innovation Agenda on Cyber-physical Systems of Systems, 2016-2025. Strategic Roadmap Document of the EU project CPSoS, Process Dynamics and Operations Group (DYN), TU Dortmund, Germany, 2016.
INOSIM Bio -New Approaches for Bioprocess Simulation and Optimization
  • K Sulzbacher
  • P Balling
  • G Schembecker
K. Sulzbacher, P. Balling, and G. Schembecker, "INOSIM Bio -New Approaches for Bioprocess Simulation and Optimization," in Computer-aided Chemical Engineering, 2013, vol. 32, pp. 865-870.