Case Study

Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence

As part of an effort to drive reliability in its refinery operations, Saras turned to Aspen Mtell® prescriptive maintenance to improve equipment uptime and decrease maintenance costs.

Case Study

Global Pulp and Paper Company Improves Production, Cuts Maintenance Costs

A global pulp and paper manufacturer set out to predict risk and improve capacity using the power of automation.

Executive Brief

将意外停机转变为计划停机,以最大限度地提高安全性、可持续性和生产率

意外停机影响广泛,所波及的不仅仅是工厂的生产率和盈利能力。强制关机还会对工厂和人员安全、温室气体排放和环境合规产生重大影响。但是,如果你真的可以计划停机时间呢?在本文中,我们将了解机器学习和预测分析的进步如何消除意外停机,使公司最大限度地减少最危险的情况,减少排放到环境中的气体量,提高生产率和盈利能力。

Case Study

Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

Learn how a large producer of synthetic rubber used Aspen ProMV to identify the cause of ongoing quality issues with its batch products. Download the case study to read how Aspen ProMV uncovered the variables that correlated most with batch quality, resolving production problems faster to limit losses.

Case Study

Multivariate Statistical Analysis Finds the Bad Actors in Light Component Losses

This petrochemical company launched an Aspen ProMV™ pilot project to investigate light component losses that go to the bottom of a fractionation column and pressurize the downstream column. Using Aspen ProMV for continuous processes, a model was developed, and bad actors that are highly correlated to the light product loss were identified. Aspen ProMV’s optimization tool was also utilized to provide better operating conditions to reduce losses.

Case Study

Multivariate Statistical Analysis Finds Cause of Quench Oil High-Viscosity Issue

One of the world's largest chemical, plastic and refining companies used Aspen ProMV to understand and resolve production problems caused by an ongoing quench oil high-viscosity issue. In this case study, learn how Aspen ProMV enabled the company to highlight the top process variables highly correlated with viscosity issues, and quickly guided process engineers to the underlying issue to limit losses.

Executive Brief

셀프 옵티마이징 플랜트: 산업용 AI가 주도하는 자율 공장의 시대

오늘날의 VUCA (Volatility, Uncertainty, Complexity and Ambiguity), 즉 시장의 변동성과 불확실성, 복잡성, 모호성이 가중되고 있는 상황에서 기업들은 인공지능(AI) 기술과 자율 및 반자율 기술을 활용하여 새로운 차원의 안전성과 지속가능성, 수익성을 달성하는 방법을 모색하고 있습니다. 이는 셀프 옵티마이징 플랜트(Self-Optimizing Plant)를 향한 여정의 시작을 의미합니다.

Executive Brief

自己最適化プラント: インダストリアルAIが支える、新時代の自律性

現在のVUCA環境において、新たなレベルの安全性、持続可能性、および収益性を実現する自律/半自律プロセスの開発のために、デジタライゼーションやAIに目を向ける企業が増えています。自己最適化プラントへのジャーニーはそこから始まります。自己最適化プラントがどのようにして以下のことを可能にするのか、是非このエグゼクティブブリーフでご覧ください。

Case Study

Global Energy Company Improves Safety and Asset Integrity with Machine Learning

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.

Ebook

Digitalization and Data Analytics: Are you Missing a Crucial Ingredient in Your Process

If you’re like most food and beverage process manufacturers, it could take you weeks or months to sift through and interpret all your data. In an industry where quality and precision is everything, delays can result in unplanned downtime, defective batches, product recalls and cost overruns—all with the potential to negatively impact your company’s reputation and bottom line.

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