Are you struggling to effectively organize and analyze your data to troubleshoot bad batches? Multivariate analytics will quickly deliver insights that can increase batch quality, yield and throughput. Manufacturers have already realized 10 percent increase in production and 50 percent reduction in quality variability through the use of such tools. Download this paper now to learn how to get truly meaningful insights from your batch process data for optimal plant performance.

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.

A AspenTech® desenvolveu um método para integrar modelos fenomenológicos de simulação de processos e o conhecimento do processo com IA e algoritmos de analytics. O resultado é um sistema de modelagem híbrida que oferece um modelo abrangente e preciso mais rapidamente sem exigir uma experiência significativa.

AspenTech® ha inventado un método para integrar modelos de simulación de procesos basados en primeros principios con el conocimiento en operación y algoritmos analíticos de IA. El software resultante de ello es un sistema de modelado híbrido que ofrece un modelo completo y preciso de forma más rápida sin necesidad de una experiencia significativa.

在白皮书里，我们探讨了如何改善炼油和石化公用事业系统运营，以提高能源效率、减少排放和提高灵活性。

Recently, Mining Magazine conducted a survey of global mining executives to better understand their attitudes towards technology. The survey explored the technology, obstacles and key opportunities that enable mining organizations to significantly increase uptime, efficiency, performance and transform auditable, data-base insights to improve processes and infrastructure.

Mining companies invest heavily in equipment for all stages of mining, mineral processing, refining and distribution. By monitoring asset condition and behavior and developing profiles of normal operations, anomalies and failures, predictive maintenance tools can notify staff of equipment problems prior to failure. This paper outlines how predictive maintenance provides mining organizations the intelligence needed to:

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:

Apesar dos esforços de manutenção abrangentes em algumas minas, ainda ocorrem falhas de equipamento. A fim de atingir um alto nível geral de confiabilidade de ativos, é essencial que as operadoras invistam regular e significativamente em equipamentos e infraestrutura.