  Data-driven soft sensors provide a potentially cost-effective and more
accurate modeling approach to measure difficult-to-measure indices in
industrial processes compared to mechanistic approaches. Artificial
intelligence (AI) techniques, such as deep learning, have become a popular soft
sensors modeling approach in the area of machine learning and big data.
However, soft sensors models based deep learning potentially lead to complex
model structures and excessive training time. In addition, industrial processes
often rely on distributed control systems (DCS) characterized by resource
constraints. Herein, guided by spatial geometric, a lightweight geometric
constructive neural network, namely LightGCNet, is proposed, which utilizes
compact angle constraint to assign the hidden parameters from dynamic
intervals. At the same time, a node pool strategy and spatial geometric
relationships are used to visualize and optimize the process of assigning
hidden parameters, enhancing interpretability. In addition, the universal
approximation property of LightGCNet is proved by spatial geometric analysis.
Two versions algorithmic implementations of LightGCNet are presented in this
article. Simulation results concerning both benchmark datasets and the ore
grinding process indicate remarkable merits of LightGCNet in terms of small
network size, fast learning speed, and sound generalization.
