AspenTech Industrial AI™는 공정 산업의 판도를 바꾸는 기술로서 AI 알고리즘의 속도와 성능에 실제 도메인 전문 지식의 효율성과 매개변수를 결합하였습니다. 특수 목적에 맞게 설계된 아스펜테크의 AI 솔루션은 데이터 인사이트, 엔지니어링 기반기술, 공정자산에 대한 지식 및 산업에 특화된 전문 지식을 결합하여 고객 기업이 끊임없이 변화하는 비즈니스 요구 사항에 더 빠르게 적응하고 대응할 수 있도록 지원합니다.

Natural gas is uniquely positioned as a transition fuel to support an affordable and secure energy transformation through displacement of coal power electricity generation and supplementing renewable electricity.

AspenTech Industrial AI™ is a game-changing technology for the process industries, combining the speed and power of AI algorithms with the efficiency and parameters of real-world domain expertise. Our purpose-built AI solutions bring together data insights, engineering fundamentals, asset knowledge and industry expertise, enabling companies to adapt and respond quicker to ever-changing business needs.

Bio-based feedstocks play an important role in creating a circular economy. Making biofuel or chemicals from biofeedstocks typically has a lower carbon footprint than the fossil fuel-based processes. Therefore, transforming biofeedstocks is considered as one of the energy transition pillars. Despite sustainability advantages, many chemical and polymer makers have been reluctant to actively pursue large-scale use of biofeedstocks mainly due to economics and process complexities.

As sustainability targets drive the energy industry towards a net zero future and energy security concerns intensify, companies are challenged to meet these competing demands within financial and workforce restraints. Owners and EPCs must cater to short-term energy demands while simultaneously positioning themselves for the long-term energy transition projects gaining traction.

世界中で、主要な組織が高度なデジタル技術を採用および実装しています。デジタルトランスフォーメーションの旅は、資産集約型産業、特にエネルギーおよび化学薬品ビジネスの性質を変えるでしょう。こうした状況下では、デジタルツイン（物理的な資産の仮想化されたコピーとその運用上の動作）が重要な役割を果たします。今日アスペンテックが描くデジタルツインの重要なコンセプトは、仮想データに対して洞察とアドバイスを提供するAIの力です。本ホワイトペーパーでは、これからのデジタルツイン戦略で重要になる鍵をご覧いただけます。

Carbon capture continues to be top of mind for energy and chemical companies as they navigate the energy transition, but what is the best approach to achieve mitigation goals? Accurate process modeling is a crucial requirement to select the right processes. In this white paper you will learn how advancements in predictive modeling technology enable you to design the most operable, energy efficient and sustainable solutions.

碳捕获仍然是能源和化工公司在能源转型过程中的首要任务，但实现减排目标的最佳方法是什么？准确的工艺建模是选择正确工艺的关键要求。在本白皮书中，您将了解预测建模技术的进步如何使您能够设计最可操作、节能和可持续的解决方案。

Today, refineries face the difficult challenge of maintaining and improving their gross refinery margin while meeting increasingly stringent emissions regulations. Refineries need to work in anticipation of new evolving emission regulations. Learn how BPCL Kochi refinery used Aspen HYSYS to develop a real-time refinery-wide digital twin for emissions monitoring enabling them to:

Across the globe, leading organizations are embracing and implementing advanced digital technologies and the industrial metaverse. This industrial digital transformation journey will change the nature of asset intensive industries, particularly the energy and chemicals businesses. In that context, digital twins — virtualized copies of physical assets and their operating behaviors — will play key roles.