This article (published in PTQ Magazine) describes how the on-line coordination and optimisation of refinery process units led to a 10% increase in middle distillate production without investing in process units or equipment.

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

Are you exploring all avenues to enhance operational productivity, efficiency and safety at your plant? Download this article to learn how Statoil introduced one standard solution for storing and presenting process data to achieve cost savings and simplification.

See how refineries can keep their profit margins high by maintaining their planning models up to date, in the face of changing operating conditions, using capabilities built in Aspen HYSYS.

Leaders often wonder, where do we apply digital technologies to the challenges in our business? How can we chart our success? This uncertainty is understandable considering the technology’s complexity and rapid evolution, but understanding and integrating artificial intelligence (AI) is increasingly critical to competitiveness in the chemical industry.

“Automation also can help identify issues before something critical fails. Finding problems as early as possible helps improve asset management,” says CSX Executive Vice President and Chief Operating Officer, Cindy Sanborn. Read this article to learn more about CSX’s exciting project with Mtell, which uses patented machine learning technologies with industrial assets to perform predictive maintenance which no longer runs parts to failure.

従来の対処的メンテナンスだけでは、不測の事態に対応できません。手軽な機械学習による資産パフォーマンス管理(APM) により、今や製造工場のスタッフが、何十年にもわたって蓄積してきた設計や運用データから容易に価値を引き出し、資産（主に装置などのハードウェア）のパフォーマンスをより適切に管理して最適化することが可能になりました。本書では、常識を覆すような画期的なテクノロジーがどのように精密な故障パターン認識を使い、高精度に装置の故障を数ヶ月も前に予測し、処方的なメンテナンスをガイダンスするかを説明しています。また、５つのベストプラクティス（運用方法）をご紹介し、最先端の信頼性管理による、さらなる生産効率および利益率の改善手法について述べています。

In this article, Robert Golightly explains the limitations of traditional maintenance practices and how new advancements in predictive maintenance are pushing assets and equipment to the limits of performance. Learn how today's industry leaders are using low-touch machine learning to extract value from design and operations data to predict when and where failures will occur.

AspenTech’s Performance Engineering Solutions can help improve engineering productivity and collaboration. Download this white paper to learn how you can leverage concurrent process engineering and lifecycle project economics to be more agile in bidding, project execution and handover of digital project data to your customer.

To be a leading 'player' today's specialty chemicals environment, overcoming these challenges is crucial to staying competitive and ensuring the entire supply chain is optimized.