Recently, two technical achievements from Wuhan Building Material Industry Design & Research Institute Co., Ltd. won the 2025 Building Materials Science and Technology Awards.

First Prize of Technical Invention Award

The project "Design, Preparation and Engineering Application Development of High-Performance Carbon-Sequestering Cementitious Materials" — in which Wuhan Building Material Institute participated as a co-developer — won the First Prize of Technical Invention Award.

The project addresses national carbon neutrality goals and the low-carbon development needs of the cement industry. It tackles the lack of key basic materials for large-scale utilization of CO? from industrial flue gases. The research revealed the synergistic relationship between the gelling?reaction kinetics of carbon?sequestering cementitious materials and CO? transport kinetics. It achieved breakthroughs in the industrial preparation of high?performance carbon?sequestering cementitious materials (γ?C?S, gamma?dicalcium silicate) that offer strong gelling ability, high carbon?sequestration capacity, and low carbon emissions. The project also developed a complete set of technologies for the design, production, and application of carbon?mineralized products based on these new materials and industrial flue gases.

Wuhan Building Material Institute contributed to the research on "Industrial preparation and application technology of low?carbon building materials based on carbon?sequestering cementitious materials and industrial flue gases." Using high?performance carbon?sequestering cementitious materials, the Institute industrially produced carbon?mineralized fiber cement boards and successfully applied them in practice.

Third Prize of Technological Progress Award

The project "Development and Application of Intelligent Production Technology and Core Equipment for High?Quality Calcium Silicate Boards" — led by Wuhan Building Material Institute — won the Third Prize of Technological Progress Award.

This project achieved a 40% increase in single?line production capacity, a 10% reduction in energy consumption, and a 20% reduction in labor requirements. The self?adaptive automatic pulp refining and control system, powered by deep neural network (DNN) and long short?term memory (LSTM) algorithms, makes fiber treatment more intelligent and efficient — a first in the industry. The project also clarified the closed?loop detection and control relationships among slurry concentration, layer thickness, and vacuum dewatering, marking the arrival of an "expert system" for board forming. In addition, the team developed a high?efficiency visual sorting system for finished products. Using AI + GPU for real?time image processing, and combining traditional algorithms with deep learning, the system achieves a defect recognition accuracy of ≥98%, completely replacing manual visual inspection. Furthermore, the project successfully resolved more than ten pain points and difficult issues that have constrained the industry's development.