Using green-hydrogen and bioethanol fuels in internal combustion engines to reduce emissions

Becerra-Ruiz, J.D.; González-Huerta, Rosa de Guadalupe; Gracida, Jorge; Amaro-Reyes, Aldo; Macías-Bobadilla, Gonzalo

doi:10.1016/j.ijhydene.2019.02.211

Cited by 32 publications

(11 citation statements)

References 15 publications

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“…The measured results in our research are consistent with those conducted by Becerra-Ruiz et al (2019). During their experiment, the direct NO X production increased up to 30 percent.…”

Section: Resultssupporting

confidence: 92%

Impacts of HHO gas utilisation on the operating parameters in internal combustion engines

Jindra¹,

Kotek²

2022

Res. Agric. Eng.

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Social pressure is forcing manufacturers to make huge investments in development in order to reduce the harmful emissions of passenger cars. However, there are also cheaper solutions that promise to reduce pollutants with minimal investment. One way is to enrich the fuel/air mixture with HHO gas (oxyhydrogen with a 2 : 1 ratio of hydrogen and oxygen). HHO gas is produced by the electrolysis of water using the vehicle's electrical system. This study investigated the impact of an HHO generator on the operating parameters of an internal combustion engine. The testing took place on two levels. The first was a laboratory test with an spark ignition (SI) engine connected to a dynamometer where complete load characteristics were measured and the function of the engine in various steady-state modes was monitored. The second was a laboratory test of a compression ignition (CI) engine vehicle in a chassis dynamometer where the engine behaviour was observed during simulated driving. During the measurements, it was clearly demonstrated that there is a serious change in the fuel/air mixture. The HHO gas mixture caused engine control instability. The control unit was not able to sufficiently regulate the engine parameters. During the test on chassis dynamometer, the steady-state regimes did not last long enough for this instability to occur.

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“…The measured results in our research are consistent with those conducted by Becerra-Ruiz et al (2019). During their experiment, the direct NO X production increased up to 30 percent.…”

Section: Resultssupporting

confidence: 92%

Impacts of HHO gas utilisation on the operating parameters in internal combustion engines

Jindra¹,

Kotek²

2022

Res. Agric. Eng.

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“…Bioethanol is well known as a promising substitute for petroleum-based gasoline (Huang et al, 2020;Amid et al, 2021), with considerably lower emissions throughout its life cycle (Mabee and Saddler, 2010). For example, Becerra-Ruiz et al (2019) reported a decrease of 99, 93, and 67% in CO, HC, and NOx, respectively, when a 5500 W portable engine generator of alternating current burned bioethanol instead of gasoline. Compared to first-generation bioethanol such as corn and sugarcane-based bioethanol, second-generation bioethanol (i.e., bioethanol produced from lignocellulosic feedstocks) has significantly lower life cycle GHG emissions (Wang et al, 2020).…”

Section: Conversion Of Forest Biomass Into Liquid Biofuelsmentioning

confidence: 99%

An Overview on the Conversion of Forest Biomass into Bioenergy

Qian

Wang

et al. 2021

Front. Energy Res.

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Biomass plays a crucial role in mitigating the concerns associated with increasing fossil fuel combustion. Among various types of biomass, forest biomass has attracted considerable attention given its abundance and variations. In this work, an overview is presented on different pathways available to convert forest biomass into bioenergy. Direct use of forest biomass could reduce carbon dioxide emissions associated with conventional energy production systems. However, there are certain drawbacks to the direct use of forest biomass, such as low energy conversion rate and soot emissions and residues. Also, lack of continuous access to biomass is a severe concern in the long-term sustainability of direct electricity generation by forest biomass. To solve this problem, co-combustion with coal, as well as pelletizing of biomass, are recommended. The co-combustion of forest biomass and coal could reduce carbon monoxide, nitrogen oxides, and sulfide emissions of the process. Forest biomass can also be converted into various liquid and gaseous biofuels through biochemical and thermochemical processes, which are reviewed and discussed herein. Despite the favorable features of forest biomass conversion processes to bioenergy, their long-term sustainability should be more extensively scrutinized by future studies using advanced sustainability assessment tools such as life cycle assessment, exergy, etc.

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“…The main advantage is that different systems can be represented in the same way, especially when studying first-order and second-order systems. Several investigations use systems of first-order or second-order [ 13 , 14 , 15 ]. The investigation [ 16 ] uses the parameter estimation of the Transfer Function instead of working with the dynamic model of the system to solve the inverse heat conduction problem.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

Rodríguez-Abreo

Velásquez

Paz

et al. 2021

Sensors

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In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic characteristics of the dynamic response were extracted and used to calculate a Transfer Function equivalent to the dynamic model. A database with 1,500,000 data points was created to train the network system with the basic characteristics of the dynamic response and the Transfer Function that causes it. The contribution of this work lies in the use of Neural Network systems to estimate the behavior of any stable system, which has multiple advantages compared to typical linear regression techniques since, although the training process is offline, the estimation can perform in real time. The results show an average 2% MSE error for the set of networks. In addition, the system was tested with physical systems to observe the performance with practical examples, achieving a precise estimation of the output with an error of less than 1% for simulated systems and high performance in real signals with the typical noise associated due to the acquisition system.

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Using green-hydrogen and bioethanol fuels in internal combustion engines to reduce emissions

Cited by 32 publications

References 15 publications

Impacts of HHO gas utilisation on the operating parameters in internal combustion engines

Impacts of HHO gas utilisation on the operating parameters in internal combustion engines

An Overview on the Conversion of Forest Biomass into Bioenergy

Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

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