Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 41

Manufacturing operations consume most of the production costs and digital technology can bring the highest impact in this area. This is true for all segments of the chemical industry, from petroleum refinery to petrochemicals to pesticides to specialty chemicals. The global management firm Mckinsey estimate the potential for a three‐ to five‐percentage point improvement in return on sales from employing digital in production operations (Klei et al., 2017). With the advent of data historians two decades ago, an enormous amount of production‐related data has been collected by all of the major chemical industries. However, due to the absence of proper data analytics software, most of these data remains unutilized. AI‐based algorithms can extract knowledge from these data and utilize that knowledge to achieve higher efficiency and throughput, lower energy consumption, and more effective maintenance. For many companies, these are low hanging fruits and benefit can be achieved immediately using existing IT (information technology) and process control systems.

The contribution to profits can be substantial. Examples are many in all leading chemical industries around the world. A major petrochemical company applied advanced AI‐based data analytics to a billion data points that it collected from its naphtha cracker manufacturing plant. With the help of an AI‐based stochastic optimization algorithm, this plant optimizes different process parameters that lead to an increase in the ethylene production by 5% without making any capital investments and generated cost savings by reducing energy consumption by 15%. A leading refinery company takes another approach at one of its main plants: it used AI‐based advanced analytics to model its production process and make a virtual plant, and then used the model to provide detailed, real‐time guidance to DCS (distributed control system) panel operators on how to adjust process parameters to optimize performance. Once it was implemented, profit from this plant increased by over 25% and yields increased by seven percentage points, thus saving on raw materials, while energy consumption fell by 26% (Holger Hürtgen, 2018).

Besides this AI‐driven analytics‐based opportunity, there are other digital‐enabled advances that have started creating profit in the manufacturing operations area. Examples include IoT‐based steam trap monitoring, IoT‐based wireless vibration and temperature monitoring of critical pumps and other single‐line rotating equipment, the use of digital sensors to monitor vent gas composition, etc. These advances help to reduce maintenance costs and improve process reliability and safety performance. At the same time, deploying a holistic automated and centralized data analytics and plant performance management system should enable the plant engineers to monitor the plant better and take proper corrective and preventive actions faster.