Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 54

Today's big chemical complexes have at least 10–20 plants and each plant consists of approximately 4000–7000 transmitters or sensors that can collect data every second (Holger Hürtgen, 2018). For instance, a petrochemical complex having 10 individual chemical plants generates 3.127 trillion data in one year. It is very costly (and perhaps impossible) to capture and save every bit of the tera‐ and petabytes of data that will be generated every second and will create a data overload on the system. Not all the data are relevant to make an impact on the business in the chemical industry. Hence it is important to know which data should be collected and at which frequency so that it can be used to generate business insights and drive the profits up. One easy solution is to follow the use case earlier implemented in any other chemical industry. Defining certain requirements based on particular use cases will help ensure that only relevant data are captured.

As shown in the study of Mckinsey, the situation requires a vision and critical thinking to find which data to store in its original granularity and which to aggregate or pre‐analyze. In the case of relevance, the more classical “hypothesis‐driven” or “use case backwards” approach often delivers better results than the often praised “Big Data, brute force” approach.

Data layering is another critical requirement while handling enormous volumes of data of the chemical industry. Instead of overloading the data analytic algorithm with all types of raw data, carefully organizing it into several logical layers and then employing a logic by which to stack these layers can help generate more meaningful data.