This chapter outlines the preliminary design of a gas-turbine-based combined heat and power (CHP) system, developed to meet the thermal and electrical demands of a beverage production facility. A techno-economic model was implemented in Python to integrate technical design with economic evaluation, assessing the feasibility of various system sizes. The approach combines thermodynamic modelling of the CHP system with analyses of capital investment, operational costs and projected cash flows, all benchmarked against a baseline scenario. Key economic indicators - including net present value (NPV) and payback period (PBP) - were employed to evaluate system performance and determine optimal configurations. Results indicate that the selection of economic metrics influences optimal system size; for instance, maximizing NPV leads to a larger system size than minimizing PBP. The chapter also serves as an educational resource, guiding readers through the application of thermodynamic principles in Python and demonstrating the link between technical and economic assessments in energy investment. The Python codes provided, though simplified, offer a foundational approach, while highlighting the potential for more advanced modelling in real-world applications.
Preliminary design of a combined heat and power system for a beverage industry
Bianco, VincenzoWriting – Review & Editing
2025-01-01
Abstract
This chapter outlines the preliminary design of a gas-turbine-based combined heat and power (CHP) system, developed to meet the thermal and electrical demands of a beverage production facility. A techno-economic model was implemented in Python to integrate technical design with economic evaluation, assessing the feasibility of various system sizes. The approach combines thermodynamic modelling of the CHP system with analyses of capital investment, operational costs and projected cash flows, all benchmarked against a baseline scenario. Key economic indicators - including net present value (NPV) and payback period (PBP) - were employed to evaluate system performance and determine optimal configurations. Results indicate that the selection of economic metrics influences optimal system size; for instance, maximizing NPV leads to a larger system size than minimizing PBP. The chapter also serves as an educational resource, guiding readers through the application of thermodynamic principles in Python and demonstrating the link between technical and economic assessments in energy investment. The Python codes provided, though simplified, offer a foundational approach, while highlighting the potential for more advanced modelling in real-world applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


