競争の激しい市場でビジネスを成長させるために、大阪に本社を置く化学品メーカーのダイセルは、特定の市場の需要を満たすために狭い分子量分布を持つポリマーを開発する必要がありました。この事例では、ダイセルがどのようにアスペンポリマーを使用してカスタムRAFT重合モデルを開発したかをご覧頂けます。このモデルにより、お客様はプロセス条件を最適化し、実験コストを削減できるようになりました。これにより、次の利点がもたらされました。

Discover how Chevron overcame the challenge of rapidly returning APC controllers to service after unit turnarounds. Using aspenONE APC applications, Chevron reduced the effort required to maintain controller models. In addition to this, Chevron saved thousands of dollars for each day the controller would have been offline.

Learn how a major European chemical producer implemented aspenONE® Supply Chain Management, integrated to their SAP ERP system, and was able to reduce inventories by 20%, achieve 97% on time deliveries, and reduce the number of campaign transitions by 20%.

LG Chem increases capacity by 15% and saves energy through heat integration using Aspen Plus®, Aspen Energy Analyzer, and Aspen Exchanger Design and Rating

Siirtec Nigi increased their sulfur production from 50 to 90 tons per day using Aspen HYSYS Sulsim to simulate the case study and conduct a sensitivity analysis around their oxygen enrichment. The new process required minimal equipment changes and no additional downtime other than turnaround.

Linde Engineering North America monitors construction costs and progress using ACCE linked to 3D models driving down the cost of the estimate.

Learn how YPFB Andina was able to increase their gas production using an integrated model solution that was able to provide a $280M increase in revenue in 1 year.

Phillips66 improves estimating productivity by 85% and reduces estimating variability to +/-15% with a five year before and after analysis of estimates vs. completed projects. Download the case study to learn more.

Shell Oil Company is a global energy and petrochemicals company that produces natural gas, gasoline, oil, and many bulk and specialty chemical products for customers worldwide. Read this case study to learn how Shell’s Manufacturing Margin Optimization team uses AspenTech solutions and AWS Cloud to optimize its energy and petrochemicals business, including planning and scheduling applications.

In this case study learn how a global oil and gas company was able to detect and predict a variety of pending equipment failures. Download today to uncover how Aspen Mtell enabled the company to correctly identify all reported events – as well as unknown problems.