http://bura.brunel.ac.uk/handle/2438/8624 2026-04-17T06:15:33Z 2026-04-17T06:15:33Z Pinto da Mota Longo, Kevin http://bura.brunel.ac.uk/handle/2438/33129 2026-04-11T02:01:04Z 2025-01-01T00:00:00Z

Title: Experimental studies on the performance of low-carbon, high-efficiency heavy-duty dual-fuel combustion engines Authors: Pinto da Mota Longo, Kevin Abstract: This thesis presents an experimental investigation into dual-fuel combustion strategies aimed at decarbonising heavy-duty engines through the use of low- and zero-carbon gaseous fuels. A single-cylinder research engine and its fuelling system were upgraded for dual-fuel operations with hythane and hydrogen. Systematic experiments were performed at a constant engine speed of 1200 rpm and loads of 0.6, 1.2, and 1.8 MPa IMEP, corresponding to 25%, 50%, and 75% of full engine load. The study explored both conventional and advanced combustion strategies by varying effective compression ratio and diesel injection timing to maximise thermal efficiency and minimise engine-out emissions. The diesel-hythane dual-fuel system demonstrated strong potential for short-term decarbonisation. An advanced combustion strategy using early diesel injection combined with Miller cycle delivered significant improvements in thermal efficiency by up to 4% at low load and reduced CO₂ emissions by up to 40% relative to conventional diesel combustion. Total GHG emissions were lowered by approximately 25%, and NOx and soot emissions were reduced by as much as 89% and 69%, respectively, compared to diesel-only operation. The diesel-hydrogen system, while facing limitations in diesel substitution due to combustion phasing constraints, achieved the highest CO₂ and GHG reductions – by up to 56% – when operated with a lower effective compression ratio. Although NOx levels increased under the baseline configuration, mitigation strategies such as external EGR, water injection, and leaner mixtures were shown to effectively reduce NOx without compromising efficiency. Notably, green hydrogen use allowed the diesel-hydrogen powertrain to exceed the EU’s 2030 CO₂ reduction target. A comparative assessment across diesel-CNG, diesel-hythane, and diesel-hydrogen systems confirmed that while methane-based fuels offer substantial NOx reduction, their GHG benefits are limited by methane slip. Hythane emerged as the best short-term solution due to its balance of efficiency and emissions performance, while green hydrogen showed the greatest promise for long-term decarbonisation, provided that NOx control strategies and injection optimisation are fully implemented. Overall, this research confirms that dual-fuel combustion with hythane and hydrogen – when paired with advanced engine strategies – can significantly lower the carbon and pollutant emissions of heavy-duty diesel engines. The findings provide a solid foundation for the further development of clean, efficient dual-fuel systems aligned with upcoming emissions regulations and climate targets. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London

2025-01-01T00:00:00Z Olabi, V Alhajeri, A Ghazal, H Jouhara, H http://bura.brunel.ac.uk/handle/2438/33117 2026-04-09T02:00:55Z 2026-04-07T00:00:00Z

Title: Dynamic multi-objective, multi-period optimisation of a hydrogen supply chain in the Gulf Cooperation Council (GCC) region: A Saudi Arabia case study Authors: Olabi, V; Alhajeri, A; Ghazal, H; Jouhara, H Abstract: Home to some of the highest solar radiation levels globally and a strategic export location, Saudi Arabia ranks among the top countries for green hydrogen potential. However, widescale deployment remains constrained by the challenge of designing a supply chain that can effectively balance trade-offs between economic, environmental, and safety/risk objectives. This study presents a multi-objective, multi-period optimisation model for the design of a green hydrogen supply chain (HSC) network in the Northwestern region of Saudi Arabia, considering various production technologies (electrolyser types), storage options, and transportation modes. A novel dynamic framework is developed to simultaneously optimise cost, carbon footprint, and safety/risk. Within this framework, a hybrid AHP–MILP approach is integrated to capture stakeholder preferences and their evolution over time through time-dependent weightings, enabling the relative importance of economic, environmental, and safety criteria to adapt across planning periods in line with changing stakeholder priorities. Four planning periods are considered in this study: establishment phase (T1); early operations phase (T2); steady operations phase (T3) and mature system (T4) - with low, medium, and high demand scenarios analysed in each period. Results showed that as hydrogen demand increases, production technologies converge in performance because their individual strengths and weaknesses counterbalance each other, while storage and transportation technologies diverge as scale amplifies the advantages of various criteria. Description: Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S036031992601476X#appsec1 .

2026-04-07T00:00:00Z Mathew, A Shapiro, S Wang, X Zhao, H http://bura.brunel.ac.uk/handle/2438/33113 2026-04-09T02:00:56Z 2025-10-19T00:00:00Z

Title: Dual-Fuel Ammonia Engines to Decarbonise Freight Operations Authors: Mathew, A; Shapiro, S; Wang, X; Zhao, H Abstract: Operators of diesel-powered locomotives face increasing pressure to reduce carbon and exhaust emissions from their operations and transition away from using fossil fuels. Compression ignition diesel engines remain the prime movers of choice for self-powered rolling stock due to their high torque and efficiency. However, the long service life of locomotives, often exceeding 70 years, limits rapid fleet replacement. This study presents a concept for a dual-fuel diesel-ammonia internal combustion engine for locomotives, as part of ongoing research at Brunel University of London. Using data from UK Class 37 locomotives supplied by their owners and operators, representative engine duty cycles were derived from On-Train Monitoring and Recording (OTMR) data for a number of operational routes. Engine data, obtained through load bank testing of a locomotive, was then mapped onto these operational route based engine duty cycles to calculate diesel fuel use and exhaust emissions for each route. Literature based ammonia:diesel fuel ratios, validated through engine testing at Brunel University of London, were then applied to generate a comparative dual-fuel dataset. Preliminary results suggest diesel consumption - and thus carbon emissions - can be reduced by 18–29%. A CAD model was developed to demonstrate integration of ammonia fuel tanks alongside one of the two original diesel fuel tanks. Ongoing work involves using Brunel University of London’s single-cylinder test engine, upgraded for ammonia fueling, to increase ammonia:diesel ratios and assess emissions impacts. The results support the potential of retrofitting existing diesel locomotives with dual-fuel capability as a transitional pathway to lower carbon rail transport.

2025-10-19T00:00:00Z Cao, G Chen, D Zhao, H Zeng, M Chen, T http://bura.brunel.ac.uk/handle/2438/33112 2026-04-09T02:00:54Z 2025-07-28T00:00:00Z

Title: Energy Consumption Prediction and Feature Contribution Analysis of Unmanned Mining Trucks Based on XGBoost and SHAP Authors: Cao, G; Chen, D; Zhao, H; Zeng, M; Chen, T Abstract: With the rapid development of mining automation, unmanned mining trucks are increasingly used in mine transportation. Accurate energy consumption prediction is crucial for optimizing energy management and control strategies. This study uses actual operational data of unmanned mining trucks and employs an XGBoost-based prediction model for energy forecasting, with SHAP used to interpret the model and quantify the contribution of each feature. Operating conditions are classified into unloaded downhill and fully loaded upslope conditions, with data analyzed by speed intervals. XGBoost models are constructed for each condition. SHAP analysis reveals that battery current and generator current significantly impact the model under unloaded downhill conditions, while battery current dominates in most speed ranges for fully loaded upslope conditions. At low speeds, generator speed has a strong influence. SHAP dependence plots show a linear relationship between battery current and energy consumption. Feature selection is performed by removing features with minimal contributions, simplifying the model and improving efficiency. The optimized model maintains predictive accuracy while reducing complexity. The results show that the XGBoost and SHAP-based model effectively predicts energy consumption, providing a basis for energy-saving optimization in smart mining operations.

2025-07-28T00:00:00Z