Work

Thermodynamic efficiency, emissions, and economic performance optimization of dual-fuel (diesel/methanol) HCCI engines: A multi-objective approach using detailed reaction mechanism and intelligent modeling

Year: 2025

Type: article

Abstract: We develop a dual-fuel HCCI engine (diesel/methanol) based on a validated detailed kinetic mechanism (27,829 reactions, 621 species) and couple it to a deep artificial neural network surrogate for rapid multi-objective optimization. The surrogate (trained on zero-D OpenSMOKE++ simulations) predicts thermodynamic efficiency, work cost, and emissions (UHC, N-based, C-based, VOC) as functions of speed, compression ratio, equivalence ratio, intake state, and fuel split. The Pareto Front is mapped using NSGA-II, delineating trade-offs among efficiency, economic, and environmental objectives and identifying 195 Pareto-optimal operating points. Single-objective optima include 0.537 thermodynamic efficiency and emissions per cycle down to 2.92 × 10 -12 g (UHC), 6.51 × 10 -13 g (N-based), 1.12 × 10 -11 g (C-based), and 1.23 × 10 -14 g (VOC). Results demonstrate that fuel composition and equivalence ratio dominate the trade-space and optimizing them yields large simultaneous gains. The approach gives fast and accurate design guidance for large-scale HCCI over diverse potential policy and cost assumptions.

Source: Energy Conversion and Management X

DOI: 10.1016/j.ecmx.2025.101440

Authors: Ali Manizadeh, Amin Sokhansanj, Mehdi Mehrpooya

Institutions: University of Tehran, Sahand University of Technology

Language: English

Topic: Advanced Combustion Engine Technologies

Subfield: Fluid Flow and Transfer Processes

Field: Chemical Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,170