Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design

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Fuel 341 (2023) 127575
Available online 10 February 2023
0016-2361/© 2023 Elsevier Ltd. All rights reserved.
Enhancing the performance of renewable biogas powered engine
employing oxyhydrogen: Optimization with desirability and
D-optimal design
Prabhakar Sharma
a
,
*
, Dhinesh Balasubramanian
b
,
*
, Chu Thanh Khai
c
,
Inbanaathan Papla Venugopal
b
, Mansoor Alruqi
d
, Femilda Josephin JS
e
, Ankit Sonthalia
f
,
*
,
Edwin Geo Varuvel
g
,
*
, Esmail Khalife
h
, R. Ravikumar
i
, Makatar Wae-Hayee
j
a
Mechanical Engineering Department, Delhi Skill and Entrepreneurship University, Delhi 110089, India
b
Department of Mechanical Engineering, Mepco Schlenk Engineering College, Tamilnadu, India
c
Faculty of Technology, Dong Nai Technology University, Bien Hoa , Dong Nai, Viet Nam
d
Department of Mechanical Engineering, College of Engineering, Shaqra University, Al Riyadh 11911, Saudi Arabia
e
Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
f
Department of Automobile Engineering, SRM Institute of Science and Technology, NCR Campus, Modi Nagar, 201204, India
g
Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
h
Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
i
Department of Mechanical and Automobile Engineering, CHRIST University, Bengalore, India
j
Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Songkhla 90110, Thailand
ARTICLE INFO
Keywords:
Oxyhydrogen
Renewable
Optimization
Biogas
D-optimal
Desirability
ABSTRACT
The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with
biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials
were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship
functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortication increased
biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was
utilized to generate mathematical formulations (models). The output of the models was predicted and compared
to the observed ndings. The prediction models showed robust prediction efciency (R
2
greater than 99.21%).
The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24◦
crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen ow rate. All
outcomes were within 3.75% of the model’s predicted output when the optimized parameters were tested
experimentally. The current research has the potential to be widely used in compression ignition engine-based
transportation systems.
1. Introduction
The world is undergoing unprecedented levels of human develop-
ment and advancement, which has stretched human activities to
encompass all parts of the ecosystem. To safeguard the environment and
human life, however, human activities and natural resources must both
function ethically and sustainably [1]. The global community has wit-
nessed several signicant monuments of international policy that have
shaped the global environment, especially the 2030 Agenda for Sus-
tainable Development and Paris Agreement on Climate Change (PACC)
[2]. The latter has established seventeen sustainable development goals
(SDGs) as a global platform and a roadmap for peace and prosperity for
people and the planet. The United Nations adopted these goals. The fast
growth of the world’s population, as well as the advancement of in-
dustrial technology, have both contributed to the accumulation of
greenhouse gases (GHGs), which has caused climate change [3].
According to the ndings of the 1.5 ◦C Global Warming Special Study
conducted by the Intergovernmental Panel on Climate Change (IPCC),
the planet must maintain a temperature rise of no more than 1.5 ◦C to
avoid having negative effects on populations and ecosystems. This aim is
still attainable, but it requires the world to reach a level of net-zero
* Corresponding authors: Department of Mechanical Engineering, Mepco Schlenk Engineering College, Tamilnadu, India (D. Balasubramanian).
E-mail addresses: prabhakar.sharma@dseu.ac.in (P. Sharma), dhineshbala91@gmail.com (D. Balasubramanian).
Contents lists available at ScienceDirect
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https://doi.org/10.1016/j.fuel.2023.127575
Received 1 August 2022; Received in revised form 27 December 2022; Accepted 20 January 2023

Fuel 341 (2023) 127575
2
emissions by the year 2050 and calls for strong, rapid, and long-term
action on the international climate front [4,5]. The PACC was agreed
upon by 195 nations in 2015, and the framework for the COP24 summit,
which went into force at the beginning of 2019, was the rst step toward
a worldwide revolution in the way energy is generated [6]. To delay or
halt the course of climate change, we must swiftly shift away from fossil
fuels and toward clean, sustainable, and renewable energy sources [7].
Incorporating waste-to-energy (WtoE) into waste management is
widely considered to be a feasible option today, as a result of the many
decades of research and industry activities that led to this conclusion. It
gives a variety of advantages [8]. The use of WtoE to replace fossil fuels
in a variety of settings may qualify it as a source of energy that is only
partially replenishable. WtoE systems refer to any approach that gen-
erates any kind of energy, such as electricity or fuels, from a waste-based
feedstock This includes producing biogas [9]. The development of bio-
energy relies heavily on the use of primary feedstock such as dairy
waste, agricultural waste, and municipal solid waste. Carbon dioxide
(CO
2
) and Methane (CH
4
) are the primary components of biogas, which
are created as a by-product of the digestive process, also known as the
decomposition of organic waste. Because the feedstock that is required
for the generation of biogas is anticipated, these processes are appro-
priate in a great number of nations throughout the world [10].
Municipal waste naturally undergoes the process of biological
dissociation, resulting in the production of biogas. The biogas produced
has the potential to become an alternative energy source that can power
remote communities in areas of the world that are not linked to the grid.
It is also suited for grid decentralization [11]. In response to the rising
cost of fossil-based fuels and the pollution caused by ICEs that utilize
them, researchers from all over the world are focusing their efforts on
fuel additives and alternative fuels [12]. The vast majority of emissions,
including carbon dioxide, carbon monoxide, and hydrocarbons, can be
traced back to carbon, which is present in the bulk of hydrocarbon fuels.
As a consequence of this, an alternative fuel that does not include carbon
would be fantastic for lowering these toxic emissions, which have
disastrous repercussions not only for the health of humans but also for
the health of the environment [13]. One example of an alternative fuel
additive is oxy-hydrogen gas. This gas is produced by the electrolysis of
water and has the potential to improve engine performance while
simultaneously lowering exhaust emissions [14]. This gas has tradi-
tionally been used in a number of applications such as air conditioners,
hazardous waste annihilation, fruit drying, welding, and heating. The
fundamental reason for its extensive use is its greater energy level as
compared to a gaseous mixture of hydrogen and oxygen [15,16].
According to a study on the use of biogas in CI engines, it was
determined that various engine operating factors greatly impact engine
emission and performance. As a consequence, adjusting these factors can
result in signicant improvements in both engine efciency and pollu-
tion levels [17,18]. Experiments were conducted by modifying the pa-
rameters one by one; nevertheless, the multivariable assessment may be
an effective strategy for gaining a thorough knowledge of the DF en-
gine’s properties. Non-linear approaches that may be utilized for
multivariate analytic study include machine learning, statistical
methods of optimization, and model prediction [19].
Response surface methodology (RSM) is an excellent tool for design
of experiments (DoE). It helps in investigating the combined impacts of
operational factors on the output responses [41]. It is one of the ap-
proaches that fall under this category. This multi-input/output statistical
strategy concurrently augments the combined impacts of several
different parameters as well as the interacting inuences of the variables
to produce the greatest possible performance from the system [20]. The
key advantage of employing this method over comprehensive factorial
design experimentation is that it includes fewer experiments and takes
less time. Though various studies applying RSM in internal combustion
engine applications have been published, relatively limited study has
been done on the combined impacts of biogas-oxyhydrogen blends and
operational parameters such as load, oxyhydrogen ow rates (OFR), and
diesel injection timings. This provides an opportunity for the current
study.
Several experts have recently reported on the benets of using
oxyhydrogen in internal combustion engines (IC) engines. Paparao and
Murugan [21] explored the working of a CI engine powered with
Jatropha-diesel blends as injected fuel while oxyhydrogen (HHO) as the
main fuel. The study reported improvement in Heat release rate (HRR)
and brake thermal efciency (BTE) 5.2% and 1.1%, respectively. The
unburnt hydrocarbon (UHC), and smoke emissions are reduced by
approximately 33% and 18.5%, respectively. Tuccar [22] employed the
HHO gas and Foeniculum Vulgare oil biodiesel to run a CI engine. The
impact of this novel fuel combination on noise, vibration, and emission
was studied. The ndings demonstrated a reduction in the overall noise
and vibrations produced by the engine. Furthermore, a reduction in CO
emissions and a rise in NOx were reported. The important studies in this
domain are listed in Table 1:
The biogas-powered engine had been gaining increased attention
from researchers owing to its reduced exhaust emission compared to a
diesel engine. Biogas can be employed in the existing CI engine in dual
fuel mode without considerable hardware modication. This preposi-
tion makes it an attractive option considering the circular economy
aspect in terms of waste to energy and reuse concept. Goga et al. [28]
reported that the introduction of biogas in CI engines leads to BTE
reduction of up to 14.68% with increased CO and HC emissions by
45.58% and 17.74%, compared to diesel-only mode. Similar results were
reported by Nayak et al. [29] depicting a reduction in BTE and an in-
crease in the CO and HC emissions. The authors reported that advancing
the biodiesel injection timing could compensate for the reduction in BTE
while improving exhaust emission. In addition to these several other
studies reported the reduction in BTE as well as power derating from
using biogas as fuel [8,30].
Nomenclature
PCP Peak cylinder pressure
OFR Oxyhydrogen ow rate
CO Carbon monoxide
BTE Brake thermal efciency
NOx Nitrogen oxides
CI Compression ignition
DC Direct current
CO2 Carbon monoxide
UHC Unburnt hydrocarbon
ppm Parts per million
SDG Sustainable Development goal
HHO Oxyhydrogen
RSM Response surface methodology
SI Spark ignition
ANOVA Analysis of variance
KOH Potassium hydroxide
Lpm Liters per minaret
HP Horsepower
◦CA bTDC Degree crank angle before top dead center
SS Stainless steel
APC Argon power cycle
OFR Oxyhydrogen ow rate
PIT Pilot injection timing
R2 Coefcient of determination
P. Sharma et al.

Fuel 341 (2023) 127575
3
1.1. Contribution of the present study
The various measures used by researchers to enhance BTE and
braking power have been conned to changing operational factors such
as injection time and pressure, which results in only moderate
improvement. The use of hydrogen as an additive to biogas remains an
expensive endeavour due to storage and safety considerations [42]. The
usage of a low-cost technology known as a dry cell oxyhydrogen
generator has shown to be an appealing choice in a biodiesel engine.
However, it had not been investigated to improve the combustion and
emission of a biogas-powered engine in DF mode. As a result, there is a
research void in this area. The proposed research is an innovative
attempt to solve this research gap by investigating the use of a low-cost
device for manufacturing HHO for blending with biogas in the engine
induction system.
Based on literature in the biogas-powered engine domain, it is
possible to infer that analyzing a large number of input elements pro-
vides more insight into an engine’s operating conditions. These multi-
variable optimizations and regression methods determine the optimal
combination of input elements to achieve the desired engine output.
However, there is only a little number of multivariate analyses of a DF
engine utilizing RSM described in the linked scholarly literature.
Furthermore, no literature on the employment of RSM for analyzing
biogas-diesel fortied with oxyhydrogen as gaseous additive powered
engines with three distinct input parameters, namely engine load,
oxyhydrogen ow rates, and pilot injection timing, was found. Given
this gap, the present study uses D-optimal design-based RSM for
analyzing the combined inuence of independent control factors (engine
load, oxyhydrogen ow rates, and pilot injection timing) on engine
performance and tailpipe emission. Regression models are used to
forecast output responses for all input variable combinations. Using the
desirability method, the research also investigates the optimal combi-
nation of input criteria for reducing pollutants and maximizing engine
efciency.
2. Materials and methods
2.1. Engine setup
Fig. 1 depicts the test engine’s schematic, complete with all in-
strumentations and accessories. Table 2 provides the technical specs for
the test engine and loading units. Apex Innovations, Pune supplied the
whole test engine facilities while the test engine was from M/s Kirloskar
Brothers ltd. The engine load was varied by employing an eddy current-
based dynamometer. The computerized test bench has the provision to
vary the engine load by controlling the electric supply to the dyna-
mometer. A U-tube type manometer is tted beside the airbox right
before the engine’s intake manifold to determine the air supply to the
engine cylinder. To monitor the fuel ow into the engine, a glass burette
with a digital stopwatch was used. The temperature of the exhaust gas
was monitored using a K-type thermocouple inserted at the starting
point of the exhaust pipe. The speed of engine speed is measured using a
non-contact type speed sensor mounted on the dynamometer shaft end.
A Type S bean load sensor was used to measure the engine loads.
2.2. Oxyhydrogen generation
Oxyhydrogen is a mixture of oxygen gas and diatomic hydrogen gas.
An oxyhydrogen gas generator, also known as an electrolytic cell, uses
an electric current to create HHO gas. Water is electrochemically con-
verted into oxygen and hydrogen as a result of ionization interactions,
leading to the formation of HHO. In the present investigation, a 31 plates
HHO gas generator was employed to fortify the biogas. A ashback
arrestor connects the wet cell generator to the engine intake manifold.
During operation, the generator can produce up to 2 lpm of HHO. A
ashback arrestor was tted in the fuel line in the case of a backre to
ensure that the ame didn’t return to the gas supply system. The main
characteristics of the test HHO gas generator and test fuels used in the
present study are listed in Table 2 and Table 3, respectively.
2.3. Soft computing approach
2.3.1. D-optimal design
A scientic approach to the design of experiments (DoE) results in a
fewer number of experimental runs. In the present work, the Design-
Expert was employed for DoE, analysis of variance (ANOVA), develop-
ment of correlation expressions, and optimization. The RSM is a
powerful mathematical program and an outstanding optimization tool
[32]. It may assess the empirical link between many factors. In this
work, the D-optimal modeling technique was used due to its simplicity,
practicality, efciency, and low number of test runs. The factors
considered in the study were denoted by x1,x2,x3,⋯⋯., xn and where
the number of factors is denoted with n. The model can be expressed in
form of main effects and a combination of main effects and interactions
[33]:
Y=βo+∑
n
i=1
βixi+∊(1)
Y=βo+∑
n
i=1
βixi+∑
k−1
i=1
∑
n
j=i+1
βijxixj+∊(2)
herein, the Y denotes the response of the model while the intercept
factor is denoted with βo, the single term of β like β1,β2,β3,⋯,βn shows
the main effect terms (Eq. (1)) while the Eq. (2) shows the second-order
terms depicting the interaction. Given so we also presume that almost all
Table 1
Related studies on biogas powered engine.
Reference Type of fuel Experiential/
Optimization
Outcomes
Kanimozhi
et al. [23]
Moringa Oleifera +
diesel +
Oxyhydrogen
Experimental (i) BSFC reduced
compared to
biodiesel(ii)
6% reduction in
NO
x
emission at low
loads
Kazim et al.
[24]
Diesel +
Oxyhydrogen
Experimental Both torque and
efciency improved
Shahjahan
et al. [25]
Gasoline +
Oxyhydrogen
Experimental (i) 5 to 7%
improvement in BTE
(ii)
Up to 11%
reduction in CO
emission
Bhave et al.
[26]
Diesel +
Oxyhydrogen
Experimental (i) Up to 37.5%
reduction in CO
emission(ii)
7.41% increase in
NO
x
emission
Paparao et al.
[21]
Jatropha biodiesel
+diesel +
oxyhydrogen
Experimental (i) 1.1%
improvement in BTE
(ii)
Up to 19.6%
increase in NO
x
(iii)
29.4% reduction in
CO emission
Kushwaha
and Ismail
[27]
Diesel +
Oxyhydrogen
Experimental Increase in BTE and
NO
x
emission was
reported.
Present study Biogas +diesel +
oxyhydrogen in dual
fuel mode
Experimental +
Optimization
(i) Increase in BTE.
(ii)
Marginal increase in
NO
x
emission.(iii)
Parametric
optimization
P. Sharma et al.

Fuel 341 (2023) 127575
4
n variances are independently uniformly distributed random factors
with zero mean and variance
σ
2 , the optimal linear neutral estimator for
the factors β,is the standard least estimator [34].

β=(XTX)−1XTY and its variance–covariance matrix form is
σ
2(XTX)−1 and its inverse
σ
−2(XTX)is the Fisher information matrix,
which describes the experimental design’s informativeness on the
parameter estimates under study. The independent control variables in
this research were engine load, oxyhydrogen ow rate, and pilot injec-
tion timing. The response parameters were engine performance pa-
rameters (BTE) and tailpipe emission (NO
x
, CO, and HC). The D-optimal
design was employed to carry out 20 experimental runs. The motive for
choosing a D-optimal design was that conventional factorial designs
require a greater number of runs to achieve the same outcomes as D-
optimal ones [35]. Moreover, the design space in conventional factorial
designs is restricted, and it contains some factors that are either
impractical or difcult to implement. D-optimal-based designs are more
viable, simpler to execute, and dependent on linear optimization [36].
The designed array developed following the D-optimal design is shown
in Table 4.
2.3.2. Data analysis using ANOVA
ANOVA is a technique to determine if the average of two data groups
differs. Samples are used in inferential analysis to infer sample size.
ANOVA and other statistical methods help one to nd if sample out-
comes are pertinent to data groups. F-value assesses both, the variation
among groups and variance within the groups. A greater F-value value
shows if the variance in the group dominates the group variation. The p-
value may be used to assess if variances among a few of the data means
are statistically substantial [37,38].
Table 5 reveals that the F-value was 1439.96 for the BTE model. This
high value suggests that it was noteworthy to warrant attention. Simi-
larly, the p-values <0.0500 suggest the model terms of the BTE model
are signicant. The model variables L, P, O, LO, P
2
, and O
2
were crucial
in this condition.
Table 6 shows the outcome of the ANOVA nding in the case of
emission data. The F-value of the CO model was 175.73, indicating that
it is signicant enough. The NO
x
model relies heavily on the model
variables L, P, O, LP, PO, L2, P2, and O2. The F-value of 155.66 in the
instance of the CO emission model indicates that the model was
Fig. 1. Schematics of test set up.
Table 2
Test engine specications of the engine.
Name Specication
Supplier and
Manufacturer
Apex innovations and M/S
Kirloskar
Stated fuel Diesel
Speed (rpm) 1500 ±50, constant speed
Test Engine Bore ×stroke (m) 0.0875 ×0.11
Rated power 7HP, 5.2 kW
Ratio of compression 17.5
Number of strokes &
cylinder
4 & 1
Number of plates 31
Plate material SS 316
Electric current ratings 13 Ampere
Oxyhydrogen
generator
Voltage 12/24 Volts
Peak output 2 L per minute
Gasket Rubber (Neoprene)
End plate material Nylon
Table 3
Main characteristics of the test fuel.
Characteristics Biogas Diesel Oxyhydrogen [26,31]
Kinematic viscosity (@40 ◦C) – 2.67 –
Cetane number – 47 –
Cold-lter plugging index (◦C) – −0.4 –
Flash point (◦C) – 117 –
Lower heating value (MJ/kg) 21.12 42 21.995
Density (kg/m
3
) @ 15 ◦C 0.91 846 0.49115
P. Sharma et al.

Fuel 341 (2023) 127575
5
signicant enough to warrant attention. The CO emission was shown to
be inuenced by all of the primary effects (L, P, O, L
2
, P
2
, and O
2
) as well
as interaction factors (LP, PO, and OL). The F-value of 139.38 in the
instance of the HC model suggests the produced model was notable. The
model parameters L, P, O, LP, OP, OL, L
2
, P
2
, and O
2
were crucial for the
development of the HC model.
3. Results and discussion
A compression ignition (CI) engine was employed in this study in a
dual-fuel model with biogas-HHO-diesel. To boost efciency and reduce
carbon-based emissions, a low-cost technology for creating oxyhydrogen
gas was used to fortify the biogas-diesel combination. The experimental
investigation was carried out using the D-optimal design, which was
followed by model construction and optimization. The ternary fuel-
driven engine’s thermal performance (BTE) and tailpipe emissions
(NO
x
, HC, and CO) were all tested. The ANOVA was employed initially
to build the connection function among parameters in the form of
algebraic expressions to serve as a prognostic model. The created models
are explained in the following paragraphs:
3.1. Engine performance models
3.1.1. BTE modsel
Fig. 2(a-b) depicts the inuence of oxyhydrogen fortication on
biogas-diesel oil-powered CI engine’s thermal efciency using two sur-
face diagrams. The current research sought to investigate the impacts of
three independent control variables (load on engine, oxyhydrogen ow
rates, and PIT) on the dependent response output i.e., BTE. The surface
diagrams depict the inuence of the control factors on the dependent
response factors in an improved way compared to the single factor at a
time (SFAT) graph. As SFAT illustrates the inuence of one factor on one
output variable and does not reveal the interaction effects of input pa-
rameters. As a consequence, surface diagrams are more suited for dis-
playing the ndings. The ndings must be considered with the outcomes
of the ANOVA in the Table 5. Because oxyhydrogen has a larger caloric
value and a quicker ame speed than biogas, using it in the present test
engine framework improves thermal efciency [39]. When added to the
diesel-biogas mix, HHO gas improves the braking power of the engine.
Among all three control factors, the load on engine showed the
highest inuence (64.85%) on BTE (Fig. 2 and Table 5), followed by PIT
(4.15%) and oxyhydrogen ow rates (2.38%). The remaining contri-
bution is attributed to interactive terms. The governor supplies a greater
quantity of fuel to maintain engine speed as the engine load rises. Higher
BTE with increased engine load is caused by a faster rate of fuel com-
bustion. The FIT given by the manufacturer was 23 ◦CA bTDC. An
advance in PIT toward the higher range led to marginal improvement in
BTE. It allowed extra time for the combustion to complete. The inter-
action effect of PIT and load, as shown in Fig. 2(a), demonstrates that the
maximal BTE could be attained at 13 kg engine loading and 25 ◦CA
bTDC PIT. Likewise, Fig. 2(b) shows that elevated engine load and
greater HHO ow contributed to improved BTE. Eq. (3) depicts the
quadratic equation for calculating BTE (%) using the independent con-
trol factors. Eq. (3) was employed to predict the BTE and the predicted
values are compared in Fig. 2(c). As almost all the data points are near to
the best t line, there is a high degree of correlation between experi-
mental and predicted values (45◦line).
Table 4
D-optimal-based design array.
Exp.
Run
Engine load,
kg
PIT,
CA bTDC
OFR,
lpm
BTE,
%
NO
x
,
ppm
CO,
ppm
HC,
ppm
1 10 26 0.3 21.35 161 120 109
2 10 23 0.3 20.14 164 125 114
3 6 20 0.7 13.75 131 144 136
4 10 26 1.1 24.41 195 115 106
5 10 26 1.1 24.4 195 116 106
6 6 26 0.3 15.45 127 143 135
7 14 23 0.7 27.98 274 142 133
8 14 20 1.1 26.21 225 135 126
9 14 23 0.7 27.95 275 142 133
10 14 23 0.3 26.47 228 144 135
11 10 20 0.7 20.19 165 126 115
12 14 20 0.3 24.78 215 150 141
13 6 23 0.3 14.14 127 147 140
14 14 26 0.3 27.89 224 144 132
15 10 23 0.3 20.12 165 125 114
16 6 26 0.7 17.18 146 135 126
17 6 20 0.3 12.14 118 155 147
18 14 23 0.7 27.96 272 141 132
19 6 23 1.1 17.32 146 125 116
20 10 20 0.7 20.2 161 126 115
Table 5
ANOVA results for performance characteristics.
Parameter BTE
Source SoS Value ’F’ p-value
Model 522.04 1439.96 <0.0001
L 345.22 8570.15 <0.0001
P 27.27 676.9 <0.0001
O 16.16 401.15 <0.0001
LP 0.002 0.0492 0.829
LO 0.6061 15.05 0.0031
PO 0.168 4.17 0.0684
L
2
0 0.0004 0.9846
P
2
0.3753 9.32 0.0122
O
2
0.6562 16.29 0.0024
Error 0.4028
Cor Total 522.44
Table 6
ANOVA results for emission characteristics.
Parameter NO
x
CO HC
Source SoS Value ’F’ p-value SoS Value ’F’ p-value SoS Value ’F’ p-value
Model 48908.95 175.73 <0.0001 2639.16 155.66 <0.0001 2985.15 139.38 <0.0001
L 31721.97 1025.78 <0.0001 45.95 24.39 0.0006 30.92 12.99 0.0048
P 2030.97 65.67 <0.0001 43.15 22.9 0.0007 42.21 17.74 0.0018
O 1164.71 37.66 0.0001 492.64 261.51 <0.0001 469.75 197.39 <0.0001
LP 51.17 1.65 0.2273 22.41 11.9 0.0062 16.08 6.76 0.0265
LO 276.77 8.95 0.0135 64.38 34.17 0.0002 99.35 41.75 <0.0001
PO 543.35 17.57 0.0019 74.21 39.4 <0.0001 112.67 47.35 <0.0001
L
2
1478.76 47.82 <0.0001 1571.5 834.19 <0.0001 1888.53 793.58 <0.0001
P
2
956.4 30.93 0.0002 32.27 17.13 0.002 23.94 10.06 0.01
O
2
1312.01 42.43 <0.0001 14.39 7.64 0.02 12.3 5.17 0.0463
Error 309.25 18.84 23.8
Cor Total 49218.2 2658 3008.95
P. Sharma et al.

Fuel 341 (2023) 127575
6
BTE = − 26.29 +1.67L+1.99P+5.73O−0.0015L×P−0.198L
×O+0.134P×O−0.000123L2−0.033P2−2.59O2(3)
herein, the acronym ’BTE’ stands for brake thermal efciency, ’PIT’ for
the timing of pilot injection, ’o’ for rate of oxyhydrogen ow, and ’l’ for
the load.
3.2. Exhaust emission models
3.2.1. No
x
emissions model
The oxides of Nitrogen (NO
x
) have been considered harmful airborne
contaminants, and the diesel combustion contributes signicantly to
NO
x
emissions. Because of increasingly rigorous emission rules, great
effort is being made to eliminate or reduce NO
x
emissions from com-
bustion engines. Owing to the complexity and unknown chemical
species involved in its synthesis, the development of kinetic model of
NO
x
emission remains a complex phenomenon [43]. The parametric
modeling described in this paper provides a simpler machine-specic
option. It does, however, provide a foundation for developing a model
that may then be utilized for optimization. This reduces NO
x
emissions
by adjusting the operating settings to a need-based optimal range. Eq.
(4) was derived in this work to estimate NO
x
emissions.
NOx = − 685.04 −17.95L+73.26P−28.54O+0.24L×P+4.24
×L×O+7.64P×O+1.19L2−1.66P2−115.85O2
(4)
Herein, the acronym ’NO
x
’ stands for nitrogen oxide monoxide, ’PIT’ for
the timing of pilot injection, ’o’ for rate of oxyhydrogen ow, and ’l’
denotes load on the engine.
Fig. 3(a) shows variation in the NO
x
emissions vis-`
a-vis engine
Fig. 2. Model of BTE; (a) 3-D surface plots for PIT vs load; (b) 3-D surface plots for rate of oxyhydrogen ow vs load; (c) Model predicted vs observed.
P. Sharma et al.

Fuel 341 (2023) 127575
7
loading and PIT. Among the control factors, the load on engine had the
most signicant inuence on NO
x
emissions. This was validated by
ANOVA results as shown in Table 6. The data analysis shows the engine
load had almost 64.86% inuence over NO
x-
type emission. On higher
load, the greater cylinder pressure and temperature led to elevated NO
x
generation. This was attributed to the oxidation of nitrogen at elevated
cylinder temperatures, forming oxides of nitrogen oxides [44]. The
addition of oxyhydrogen and increase in NO
x
were shown to be directly
linked in the Fig. 3(b). The impact of PIT variation on NO
x
emissions was
quite modest. As shown in Fig. 3(a), the highest NO
x
emissions were
recorded at advanced PIT (25◦CA bTDC) and 100% load. The surface
diagrams show the cumulative inuence of operational settings. The
lowest NO
x
emission levels were reported at 6 kg engine loading, 20◦
injection advance, and 0.3 lpm rate of oxyhydrogen ow. Eq. (4) was
used to predict the emission of NO
x
emissions values over different
settings of engine operation. The comparison of model forecasted and
actual NO
x
values is depicted in Fig. 3(c). A robust prediction efciency
was observed in the results.
3.2.2. CO emission model
The variations in CO emission values with engine loading and PIT
change are depicted in Fig. 4(a). The data acquired in the experimental
phase shows that the load on the engine had the most substantial in-
uence on CO emission. The ANOVA outcomes (ref Table 6) show that
engine load and its quadratic component contributed to almost 61.28%
of CO emissions. However, the effects of engine load were not
Fig. 3. Model of NO
x
; (a) 3-D surface plots for PIT vs load; (b) 3-D surface plots for rate of oxyhydrogen ow vs load; (c) Model predicted vs observed.
P. Sharma et al.

Fuel 341 (2023) 127575
8
proportional, as shown by ANOVA ndings, because the second order
component was signicantly bigger compared to the main effect term. It
was observed that CO emission rst fell up to 10 kg engine load before
rising again. Similar tendencies were seen in the case of PIT. At 23.5 ◦CA
bTDC, the CO emission was the lowest. The joint effects of load in engine
and PIT, as shown in Fig. 4(a) suggest that the least CO was observed
around 10.5 kg engine load and injection advance at 23 ◦CA bTDC. The
change in rated PIT affected the delay in ignition, resulting in poor
combustion. Aside from the late initiation of fuel mixture burning in
cracks, incomplete combustion is also a signicant reason of CO emis-
sion [45]. Because more oxygen had been made available in the cylinder
during combustion, the levels of CO decreased, making the mixture lean.
The fortication of biogas with oxyhydrogen resulted in decreased CO
emissions overall (Fig. 4(b)). Additionally, the presence OH radical in
HHO is a potent oxidizer, leading to higher combustion temperature,
promoting CO to CO
2
conversion in the presence of oxyhydrogen.
Hence, the overall CO emission was decreased.
As indicated in Eq. (5), the correlation between CO and independent
control variables is shown as:
CO =516.96 −29.034L−18.26P−86.1O+0.16L×P+2.04L
×O+2.83P×O+1.23L2+0.31P2−12.13O2
(5)
herein, the letters ’CO’ stand for carbon monoxide, ’PIT’ for the timing
of pilot injection, ’o’ for rate of oxyhydrogen ow, and ’l’ denotes load
Fig. 4. Model of CO; (a) 3-D surface plots for PIT vs load; (b) 3-D surface plots for rate of oxyhydrogen ow vs load; (c) Model predicted vs observed.
P. Sharma et al.

Fuel 341 (2023) 127575
9
on the engine.
Fig. 4(c) shows a comparative analysis between model-predicted and
observed CO levels. The ndings revealed a strong predictive efcacy.
3.2.3. HC emissions model
Based on the design matrix, the observed data of HC emissions was
used to construct a quadratic equation, as depicted in Eq. (6):
HC =505.34 −31.28*L−16.53*P−106.98*O+0.135*L*P
+2.54*L*O+3.48*P*O+1.343*L2+0.262*P2−11.22*O2
(6)
wherein, the abbreviation ’HC’ stand for unburned hydrocarbon emis-
sion, ’p’ represents pilot injection timings, ’o’ rate of oxyhydrogen ow,
and ’l’ denotes load on engine
Fig. 5(a) shows the impact of PIT and load changes on the HC
emissions. A signicant of engine load over HC emissions were
observed. The ANOVA outcomes listed in Table 5 also show that the
second order term of engine load contributed 63.26%, and the same is
reected in the parabolic nature of the surfaces diagrams shown in Fig. 5
(a) and Fig. 5(b). The inuence of engine load, on the other hand,
resulted in a drop in HC emissions only up to 10 kg engine load. On
increasing the engine load, the HC emissions again augmented up to the
full load position. The lowest emission has been observed at a 10 kg load
with 25 ◦CA bTDC as shown in Fig. 5(a). Any alterations to the PIT were
found to change the ignition timing and thus causing combustion delays,
resulting in poor combustion [46,47]. Incomplete combustion owing to
delayed fuel combustion is a major cause of HC emissions [48,49].
Fig. 5. Model of HC; (a) 3-D surface plots for PIT vs load; (b) 3-D surface plots for rate of oxyhydrogen ow vs load; (c) Model predicted vs observed.
P. Sharma et al.

Fuel 341 (2023) 127575
10
HC levels decreased when higher amount of oxygen was available in
the cylinder as a result of oxyhydrogen induction. The presence of
higher-quality fuel and more oxygen particles aids in the decrease of HC
during operation of engine [50]. The interaction impacts of load and rate
of oxyhydrogen ow is depicted in the Fig. 5(b). Enhancing load on
engine with rate of oxyhydrogen ow helped to reduce HC emissions.
Greater HC reduction was observed during incidence of elevated cylin-
der temperature, which stimulates better combustion. The HC emissions
were predicted using Eq. (6). The observed UHC emission levels are
compared to the predicted HC values in Fig. 5(c). The D-optimal based
HC model was shown to be successful in forecasting HC emission levels.
3.3. Statistical evaluation of the models
Three statistical indices R
2
, adjusted R
2
, and predicted R
2
were
employed in the present study to evaluate the developed models. The
coefcient of determination (R
2
) measures the amount of variation
explained by a regression model. A regression model’s R
2
is positive if
the model’s forecast is better than the mean of the given ’y’ values;
otherwise, it is negative. When we add new predictors to a multiple
regression model, R
2
increases or remains constant, even if the new
predictors are unrelated to the target variable and provide no value to
the model’s predicting power. R- square’s disadvantage is reduced with
adjusted R-squared. It increases only if the extra predictor improves the
model’s prediction power. The adjusted R
2
drops as independent and
irrelevant components are added to a regression model. The predicted
R
2
evaluates how well a regression model predicts the responses of new
data. When the model ts the original data but is less capable of pro-
ducing effective predictions for further observations, this value is
higher. The R
2
, adjusted R
2
, and predicted R
2
of all ve models devel-
oped in the present study are shown in Fig. 6. The high values (greater
than 0.9258) of these indices in all cases show the robust forecasting
capability of the RSM-based D-optimal design.
3.4. Desirability-based optimization
A desirability technique was employed in the present research to
optimize independent control parameters (namely engine load, oxyhy-
drogen ow rates, and pilot injection timing) for the best combination of
output parameters (viz. BTE, NO
x
, carbon mono oxide, and HC). Using
Design-Expert commercial software, all inputs were converted to a
dimensionless value of desirability (z) ranging from 0 to 1. The value of
desirability as z =0 implies an unacceptable result, while z =1
represents an exceedingly good response. Depending on the criteria of
the issue, the goal of each result is to minimize or maximize, the range,
goal, and/or equal to. In the present study, all three control factors were
kept within range while the BTE was desired to be maximum. The
exhaust emission indices were dened to be minimum. Table 7 lists the
desirability determined for each variable in the current study.
The implementation of the multi-input/output optimizations tech-
nique employing the desirability technique entails combining many
outputs into a dimensionless entity of performance known as the func-
tion of desirability (FD). Fig. 7(a) displays the desirability values ach-
ieved in this investigation.
The desirability-based optimized engine operating parameters ach-
ieved were 10.88 kg engine load, 24 ◦CA bTDC PIT, and 1.1 lpm rate of
oxyhydrogen ow. The performance output of the study was 24.47%
BTE, 204.32 ppm NO
x
, 113.63 ppm CO, and 103.83 ppm HC emissions,
at the optimized setting.
3.5. Experimental validation
After the optimal parameters of engine operation were determined
using ANOVA and desirability, the model output was veried using lab-
based testing. The engine was operated at an ideal operating congu-
ration, and the performance was recorded. Table 8 displays the optimum
operating parameters, model optimized output, test results, and errors in
the model projected output. The predicted values of the model were
determined to be within 3.75% of the actual value, demonstrating a
reliable modelling strategy.
4. Conclusions
The combustion and emission characteristics of a dual-fuel CI engine
with biogas as the primary fuel, diesel as the pilot-injected fuel, and
oxyhydrogen as a fortifying agent were investigated. The trials were
conducted using an RSM-based D-optimal design. The correlation
function between control variables and output was created using
Fig. 6. Statistical indices.
Table 7
Desirability.
Control factor Response variables
Parameter Engine
load
OFR PIT BTE NO
x
CO HC
Desire In range In range In range Max. Min. Min. Min.
P. Sharma et al.

Fuel 341 (2023) 127575
11
ANOVA. The key ndings of the study were as follows:
o The inclusion of oxyhydrogen enhanced the combustion in biogas-
diesel fuelled engines. Improved fuel economy and reduced
carbon-based emission levels were observed with a marginal in-
crease in NO
x
levels.
o RSM-ANOVA was used to create mathematical expressions (models)
for each outcome. The models were used to forecast output and were
compared to observed results. All of the models showed robust pre-
diction efciency (R
2
greater than 99.21%).
o The optimal engine operating parameters achieved through
desirability-based optimization were 24◦crank angles advance,
10.88 kg engine loading, and 1.1 lpm rate of oxyhydrogen supply.
o Experiment testing corroborated the optimized settings, and all
outcomes were within 3.75% of the model-forecasted output.
Present study has the prospects to be widely utilised in diesel engine-
fueled transportation systems. The economic elements should be
examined as a future scope for the present study.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
The authors would like to thank the Deanship of Scientic Research
at Shaqra University for supporting this work.
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Further reading
[40] Sharma P. Articial intelligence-based model prediction of biodiesel-fueled engine
performance and emission characteristics: A comparative evaluation of gene
expression programming and articial neural network. Heat Transfer 2021;50:
5563–87. https://doi.org/10.1002/htj.22138.
P. Sharma et al.