Sep 1, 2023 · ML plays a pivotal role in process systems engineering by enabling data-driven decision-making, enhanced process modeling, optimal operation, fault detection and diagnosis, control strategies, process optimization, and design.

In control and systems engineering, data-driven based modeling is described through a system identification process that involves acquiring input-output data, selecting a model class, estimating model parameters, and then validating the estimated model.

4 days ago · This paper presents a Model-on-Demand (MoD) approach to system identification and its integration with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework. MoD estimation represents a hybrid of local and global modeling techniques, judiciously formulated to take advantage of both while not being computationally demanding. The 3DoF-KF MPC algorithm ...

Dec 5, 2020 · This study presented an integrated deep-learning-based and plant data-driven framework for process modeling not only to overcome the problem of lack of a feasible mechanistic model but also to gain better process understanding.

Dec 15, 2017 · Data-driven modeling and applications in plant-wide processes have recently caught much attention in both academy and industry. This paper provides a systematic review on data-driven modeling and monitoring for plant-wide processes.

Apr 14, 2025 · By utilizing the element information of the model, the call for geometric modeling functions is driven, indirectly mapping the manufacturing process by expressing the product’s state.

Data-driven model has been widely used in process industry; the process data in complex process industry has timeliness, collinearity and correlation, which is difficult to explain. This paper optimizes the use of process data based on models and data, and explains its …

Sep 12, 2024 · Wang et al. 9 reviewed the data-driven process modeling, e.g., geometry of molten pool and bead, data-driven structure modeling, e.g., grain structure and geometric distortion, and...

Learn how to align the data elements and activities of a process model with the entities and attributes of a data model to improve your business processes based on data insights.

2 days ago · Process-based models (PBMs) and deep learning (DL) are two key approaches in agricultural modelling, each offering distinct advantages and limitations. PBMs provide mechanistic insights based on physical and biological principles, ensuring interpretability and scientific rigour. However, they often struggle with scalability, parameterisation, and adaptation to heterogeneous environments. In ...