Data-driven models play a key role in predictive control and are commonly used for variable prediction, with regression models being a prevalent choice. A regression model provides a function that describes the relationship between one or more independent variables and a response, dependent, or target variable. For example, the relationship between height and weight may be described by a linear regression model. These models harness process data, often termed 'big data,' to distill valuable insights. They encompass linear, multilinear, or polynomial regression models, artificial neural networks, and transfer functions. Data-driven models are occasionally referred to as 'black-box' models because they encapsulate process knowledge within their parameters and equation structures, making it inaccessible or concealed.
(Source: Between the Poles of Data-Driven and Mechanistic Modeling for Process Operation & A review of mechanistic and data-driven models of aerobic granular sludge)