Ron Beck speaks to the power of a model based estimating system providing insight to its attributes like reusable templates and saleable features. How does ACCE differ from my proprietary Excel-based spreadsheets?

Whether it’s a pilot plant or a complex petrochemical plant, these facilities rarely run like a well-oiled machine. There are always unknown variable dependencies that are difficult to understand, and there’s substantial room for improvement. In many situations, optimization is not the focus for plants because there are issues with reliability and safety that take precedence. Does this sound familiar? The ability to simplify job tasks and boost efficiency will greatly enhance plant performance but also, make your job easier

Whether particles are being formed, reduced in size, enlarged, participating in reactions, or just being separated from a fluid stream, ignoring or poorly modeling the solids processing steps may lead to lost opportunities, including cost reductions and quality improvements. The main challenges that arise when optimizing or troubleshooting a solids process include inefficient designs due to separate modeling of fluids and solids sections, overdesign of equipment, high-energy demands, reduced yields and quality variability. Modeling the solids section of a process is important for many common processes including specialty chemicals, agrochemicals, metals and mining, pharmaceuticals, biofuels and more. This paper describes the approach for incorporating granular solids and the corresponding solids processing steps when modeling processes.

Las compañías mineras invierten fuertemente en activos para todas las etapas del proceso minero: extracción, procesamiento, refinería y distribución del mineral. Al monitorear la condición y el comportamiento de los activos y desarrollar perfiles de operaciones normales, anomalías y fallas, las herramientas de mantenimiento predictivo pueden notificar al personal sobre los problemas del equipo antes de que ocurra falla. Este documento describe cómo el mantenimiento predictivo proporciona a las organizaciones mineras la inteligencia necesaria para:

Las soluciones de ingeniería de rendimiento de AspenTech le ayudan a mejorar la productividad y la colaboración de ingeniería. Descargue este White Paper para aprender cómo puede aprovechar la ingeniería de procesos concurrentes y la optimización del ciclo de vida de los proyectos para ser más ágil en la licitación, la ejecución del proyecto y la transferencia de datos digitales del proyecto a su cliente.

Multiphase flow through pipes is characterized by the flow regime, liquid hold-up and pressure gradient. Accurate prediction of these flow attributes is necessary for designing and maintaining pipelines and flowlines in the oil and gas industry. Aspen HYSYS provides a number of flow correlations for modeling multiphase pipe flow. To aid the user and provide a basis for comparison, the performance of correlations for different pipeline geometries, fluids, and flow regimes was analyzed. This paper discusses these analyses.

Achieve savings in both engineering hours and CAPEX with modular design through the use of stanadardized designs in re-useable templates.

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.

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. This white paper describes five best practices for driving state-of-the-art reliability management to predict breakdowns months in advance—increasing production and profitability.

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理，现在可能会从数十种程序、资产和维护数据中抽取相关数值，从而优化资产绩效。在本白皮书中，将学习这种插断性技术如何部署精确性故障模式识别，其具有较高的准确性，可以提前预测设备停机月数，并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践，以期增产提盈。