Title: Exergy fuel cell micro combined heat and power systems using COA-HDNN hybrid technique

Authors: Rajesh G. Bodkhe; Rakesh L. Shrivastava; Shenbagaraman Shunmugaramalingam; Kanimozhi Kannabiran

Addresses: Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur-441110, Maharashtra, India ' Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, MS, India ' Department of Mechanical Engineering, Saveetha Engineering College, Thandalam, Chennai, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, NPR College of Engineering and Technology, Dindigul, Tamilnadu, India

Abstract: This manuscript introduces the COA-HDNN technique, which combines Hamiltonian deep neural networks (HDNN) with the crayfish optimisation algorithm (COA). The COA is to optimise energy management systems (EMS) for micro-combined heat and power (μCHP) systems with fuel cells (FC) and thermoelectric devices (TED). HDNN predicts energy demand and system performance metrics. Implemented in MATLAB, the COA-HDNN method outperforms existing approaches, including the salp swarm algorithm (SSA), particle swarm optimisation (PSO), and heap based optimisation (HBO). The results demonstrate its efficiency at 98%, cost of 2.6 × 105$, and computation time of 2.19 s, highlighting significant improvements in performance and operational efficiency.

Keywords: fuel cell; FC; thermoelectric device; micro combined heat and power; system; thermoelectric generator; Hamiltonian deep neural networks; HDNN; crayfish optimisation algorithm; COA.

DOI: 10.1504/IJEX.2025.146408

International Journal of Exergy, 2025 Vol.47 No.1, pp.31 - 48

Received: 13 May 2024
Accepted: 21 Aug 2024
Published online: 28 May 2025 *

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