The implementation of the calcium silicate roadmap requires measures in the area of digitalization, among others. Here, a reduction in energy consumption and CO2 emissions is possible through optimal operation of autoclave systems. The steam system is the core of the autoclaving process in every calcium silicate plant. In most calcium silicate plants, the system technology and control are fundamentally similar in structure. However, different system details and process specifics result in plant-specific behavior of the steam system. Due to the high process complexity and system dynamics\u2014particularly during transfer operations\u2014a completely algorithmic modeling can only be achieved with considerable experimental effort. Consequently, the correct operating parameters for the systems can only be estimated from empirical values. Unfortunately, energy efficiency is often sacrificed in favor of throughput. This research project therefore aims to investigate a less resource-intensive methodology using artificial intelligence to achieve energy-optimal operation of autoclave systems. For this purpose, AI software is to learn the plant-specific behavior of the steam systems from existing production data. A so-called “AI agent” will be trained, which then provides recommendations for energy-optimal and process-safe system operation during production. <\/p>\n\n
Objectives<\/h3>\n\n
The project goal is to create a solution that enables producers to save energy without capital-intensive systems or system expansions and without productivity losses. The result for each individual plant is software that supports autoclave operation in real time. In addition to predicting actual curing times, recommendations are provided for energy-optimal and process-safe operation of the curing process. If successful, the calcium silicate plant can save energy and CO2 during autoclaving without productivity losses. A formalized process allows for easy transfer to additional plants. The transfer takes place through several action packages (seminars, consulting, technical publications, conferences and congresses, etc.).<\/p>\n
Project Description The implementation of the calcium silicate roadmap requires measures in the area of digitalization, among others. Here, a reduction in energy consumption and CO2 emissions is possible through optimal operation of autoclave systems. The steam system is the core of the autoclaving process in every calcium silicate plant. In most calcium silicate plants, […]<\/p>\n","protected":false},"featured_media":819,"template":"","meta":{"is_featured":false,"exhibit_type":[],"show_on_map":false,"preferred_category":1,"gps_location":{"lat":48.76609018541975,"lng":11.43505096435547},"zoom":14,"location_name":"","logo_media_id":474,"logo_media_url":"https:\/\/thi-science.de\/wp-content\/uploads\/2025\/08\/csm_thi_AImotion_wb_RGB_90cd013627.webp","project_leaders":"Prof. Dr. Peter Schuderer, Prof. Dr.-Ing. J\u00f6rg Franke, Dr.-Ing. Wolfgang Eden","project_website_url":"https:\/\/www.thi.de\/forschung\/aimotion\/researchaimotion\/forschungsprojekte\/akki-ks\/"},"categories":[68],"tags":[90,97,89,96],"class_list":["post-815","post_type_projects","type-post_type_projects","status-publish","has-post-thumbnail","hentry","category-ai-and-digitalization","tag-building-material-production","tag-calcium-silicate-industry","tag-energy-optimisation","tag-reinforcement-learning"],"yoast_head":"\n
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