An Integrated Decision- Support Framework for Optimising Industrial Hall Design - A Case Study of Valmet

Abstract

Industrial machine halls are large-volume buildings with demanding spatial, structural, energy, and operational requirements. Decisions made at the early design stage, especially those related to basement configuration, can have a major effect on energy demand, embodied carbon, construction cost, operating cost, safety, and maintenance accessibility. This thesis develops an integrated decision-support framework to help compare industrial hall design alternatives during the early design stage. The framework uses Key Performance Indicators (KPIs) across four performance dimensions: energy, environmental, economic, and operational performance. The framework is applied to a Valmet tissue machine hall case study. Valmet is a global technology company that provides process technologies, automation, and services for the pulp, paper, and energy industries. In this study, three tissue machine hall alternatives are compared: a two-pit solution, an extended basement solution, and a full basement solution. These alternatives are evaluated under Swedish and Spanish climate scenarios. The energy assessment considers heating demand and ventilation fan electricity, while the environmental assessment focuses on embodied carbon from structural materials. The economic assessment includes CAPEX and OPEX, and the operational assessment evaluates safety and maintenance accessibility using structured scoring. The results show that no single alternative performs best in all dimensions. The two-pit solution has the lowest energy use, embodied carbon, and operating cost, but scores lowest in operational performance. The full basement solution performs best in terms of safety and maintenance accessibility, but has the highest material use, embodied carbon, construction cost, and energy demand. The extended basement solution provides the most balanced performance overall. The study shows that a KPI-based framework can support clearer and more systematic early-stage decision-making by making trade-offs between different performance dimensions visible.