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Packaging solutions tailored to your product needs and production processes. Innovation and experience, industry leaders.
«PROTOTYPE PROJECT OF ADVANCED VERTICAL MULTI-FORMAT AUTOMATIC PACKER FOR THE FOOD SECTOR co-financed by the European Union through the European Regional Development Fund, within the Operational Program of the Valencian Community 2014-2020 (R&D OF SMEs (PIDI-CV)»
“Through this project, COALZA has had the general objective of researching and developing a prototype of a high-performance multi-format automatic case packer for the food sector and other sectors, thanks to its flexible adaptation productively, by managing to fit different formats and product specifications.”
FILE: IMIDTA/2021/6
PROJECT AMOUNT: €115,506.21
GRANTED AMOUNT: €40,427.17
“COALZA SYSTEMS SOCIEDAD LIMITADA” has been a beneficiary of the European Regional Development Fund whose objective is to improve the use and quality of information and communication technologies and access to them and thanks to the one that has developed the “Presence website through its own page”, “Online promotion service through a payment system (SEM)” and “E-mail marketing solutions”, to improve the company’s competitiveness and productivity. From 06/15/2022 to 12/31/2022. For this, it has had the support of the TICCámaras program of the Valencia Chamber of Commerce.
A way of making Europe.
COALZA SYSTEM, S.L. has received a grant from the Comunitat Valenciana Program European Regional Development Fund (ERDF) 2021-2027Programa
AMOUNT RECEIVED: 146,468€
‘DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE-ASSISTED PALLET STACKING METHODOLOGY FOR GRAIN PRODUCTS: CASE STUDY WITH ICE CUBES’.
File no: INNCAD/2024/193
Programme: Consolidation of the business value chain. European Regional Development Fund (ERDF) Comunitat Valenciana 2021-2027.
Investment: 146.467,69 €.
Carried out by: COALZA SYSTEM, S.L.
Aim of the project: The main objective of the project is to develop and validate an advanced pallet stacking methodology assisted by artificial intelligence (AI), focused on optimising the arrangement of products in grain, with an initial case study on ice cubes.
This methodology seeks to overcome current technological barriers related to space efficiency, stacking stability and adaptability to various storage and transport conditions. The proposed AI tool will dynamically adjust stacking parameters in real time, based on specific product characteristics and environmental conditions, thus improving operational efficiency and safety in the storage and transportation of grain products.
