現実プラントで測定できるプロセスの値は、連続プラントにおいても常に変動しているため、Aspen PlusやHYSYSのシミュレーションの挙動と一致させるのは容易ではありません。このセッションでは、シミュレーションの挙動を現実プラントと一致させるためのPlant Data機能 (プラントヒストリアンからのタグデータインポート、データコンディショニング、定常状態判定など)についてご紹介します。Plant Data機能は、プロセス性能の経時的なモニタリングにも活用でき、例えば長期的に変動する熱交換器の汚れや反応器の触媒活性のモニタリングに役立ちます。

Sustainability is emerging as a critical business topic, as companies focus resources toward lowering emissions, waste and energy use in their production process. In this article, Paige Morse, Chemicals Industry Marketing Director, shares how sustainability affects four key areas within a processing plant and how digital transformation supports sustainability efforts through efficiency improvements.

See how you can use Aspen Technology solutions to develop a refinery wide process model that would enable refinery process engineers to evaluate the economic impact of operational changes and help planners achieve a more accurate assessment of profit margins.

As all companies have been challenged by the current economic and health crisis, the successful recovery requires a new view toward future development and growth with metrics that consider broad and more long-term goals. Featured in Hydrocarbon Processing, AspenTech’s Paige Morse shares considerations for building future resiliency that center around digitalization.

The 2015 Paris Agreement and other international climate change accords have helped raise global awareness of the need for countries to take urgent action to combat climate change. As companies continue to focus on sustainability, a common theme is taking shape around the role of digitalization as a key enabler. In fact, a recent ARC Advisory Group survey revealed that 75% of respondents rated digital transformation as highly important for achieving sustainability goals.

2015年《巴黎协定》和其他国际气候变化协定有助于提高全球对各国需要采取紧急行动应对气候变化的认识。随着企业继续关注可持续性，围绕数字化作为关键推动因素的作用，一个共同的主题正在形成。事实上，ARC咨询集团最近的一项调查显示，75%的受访者认为数字化转型对于实现可持续发展目标非常重要。

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.

Rigorous heat exchanger models enhance the fidelity of process flowsheets enabling you to reduce CAPEX, OPEX, and energy consumption. This guide teaches you how to convert simple heat exchanger models to rigorous models in Aspen Plus.

Find out how a new generation of technologies is creating opportunities within the process industries that were previously impossible.

Aspen Plusのバッチ晶析モデルについて紹介します。速度論型の核生成/成長モデルを考慮することで、蒸発晶析や冷却晶析、貧溶媒晶析など多種に渡る晶析プロセスを取り扱うことができます。本モデルを活用することで、バッチ晶析プロセスにおける実験回数の削減、運転条件の最適化検討などが可能となります。