Homogeneous palladium-catalyzed enantioselective hydrogenation of 5-methylenhydantoin for the synthesis of L-Valine

Article abstract of DOI:10.1016/j.jorganchem.2020.121572

In this article, we present the development of a synthetic methodology based on homogeneous catalysis for the preparation of enantioenriched L-Valine aminoacid. The enantioselective hydrogenation of 5-methylenhydantoin has been developed through broad screenings of chiral ligands, metal precursors and reaction conditions including scale-up experiments and recyclability studies. A palladium catalyzed asymmetric hydrogenation of 5-methylenhydantoin afforded the corresponding hydrogenated product in a 70% enantiomeric excess using a substrate/catalyst ratio of 500/1. A partial racemization was observed upon hydrolysis and recovery of L-Valine.

Full text of DOI:10.1016/j.jorganchem.2020.121572

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Energy Procedia 61 (2014) 595 – 598
th
The 6 International Conference on Applied Energy – ICAE2014
CO Allocation for Scheduling Enhanced Oil Recovery
2
(EOR) Operations with Geological Sequestration using
Discrete-Time Optimization
a
b
c
John Frederick D. Tapia , Jui-Yuan Lee , Raymond E. H. Ooi ,
c
a
Dominic C. Y. Foo and Raymond R. Tan *
a
Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, Malate, Manila, 1004, Philippines.
b
Department of Chemical Engineering, National Taiwan University. Taipei 10617, Taiwan, ROC.
c
Department of Chemical and Environmental Engineering, University of Nottingham Malaysia Campus. Malaysia, Broga Road,
3500 Semenyih, Selangor, Malaysia.
4
Abstract
Carbon capture and storage (CCS) can be carried out in conjunction with enhanced oil recovery (EOR) to yield
complementary environmental and economic gains. Thus, CCS in combination with EOR will provide economical
value from incremental oil recovery besides providing source for CO sequestration. Given a fixed CO supply to be
2 2
distributed to different reservoirs, it is necessary to develop an allocation model to maximize profit from EOR
operations. In this study, a discrete-time optimization model is developed subject to scheduling, capacity and flow
rate constraints. A case study is presented to illustrate the model.
©
2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(ht 2t p 0: /1/ c4 r ePa ut i bv el ic sohme md ob ny s .Eo rl gs /e l vi ci ee nr s Le st /db .y -Sn ec l- en cd t/ i3o. 0n / )a. nd/or peer-review under responsibility of ICAE
Peer-review under responsibility of the Organizing Committee of ICAE2014
Keywords:CO capture and storage; enhanced oil recovery; optimization.
©
2
1
. Introduction
CO
gain additional profit from incremental oil production. Planning for efficient utilization of captured CO
necessary for viable EOR operations. Hence, CO supply should be allocated to different reservoirs to
2
injection in depleted reservoirs can be coupled with enhanced oil recovery (EOR) operations to
2
is
2
achieve optimum results in terms of increasing revenues from existing reservoirs. Optimization models
have been proposed for such problems [1]. Various models have also been developed for CCS source-
sink matching for sequestration purposes [2-4]. These models addressed problems with the economics of
CCS alone and CCS in combination with EOR, but lack consideration of the optimal scheduling of
*
Corresponding author. E-mail address: raymond.tan@dlsu.edu.ph
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committee of ICAE2014
doi:10.1016/j.egypro.2014.11.1189

5
96
John Frederick D. Tapia et al. / Energy Procedia 61 (2014) 595 – 598
commencement of EOR for each oil reservoir. In the context of this study, optimal scheduling for oil
reservoirs is defined as a trade-off between of reservoir life against decision to invest in EOR application
which is a capital-intensive. In this study, a mixed integer linear program (MILP) is developed for
allocating available captured CO
operation duration or availability of continuous CO
plant (assuming captured CO soure) or field life for natural CO
2
to different EOR operations subject to fixed operation. In reality, the
supply is subjected to the operating life of the power
supply from underground deposits. The
2
2
2
rest of the paper is organized as follows. Section 2 gives the formal problem statement while Section 3
shows the model. Section 4 shows a case study and Section 5 gives conclusions and future works.
Nomenclature
Parameters
F
T
2
Total CO Flow Rate Available (Mt/y)
T
Y
V
Total Source Operating Life (y)
Oil Yield (Mbbls/Mt)
Oil value (M$/Mbbls)
i
i
I
S
C
(
(
k
Present worth Factor at time k
Sequestration parameter: total amount of CO
Capacity of sink j
Minimum flow rate for sink i (Mt/y)
Maximum flow rate for sink i (Mt/y)
Carbon Credits (M$/Mt)
i
2 2
stored per total CO sequestered
i
min
F
F )
i
i
)
max
C*
ΔT
i
Duration of EOR operation
early
late
(
(
T
T
i
)
i
)
Earliest time at which the operation can start (y)
Latest time at which the operation can start (y)
Distance for the source to the reservoir
Fixed cost for CO transportation (million $)
2
Variable cost proportionality constant for CO
Length of one time period
D
i
pp
(C
o
)
pp
(C)
2 2
transportation (M$/ km-Mt CO )
ΔK
Variables
f
f
ik
CO
CO
2
flow rate to sink i at time k.
flow rate to sink i.
i
2
b
ik
Binary variable indicating the existence of an EOR operation at time k
a
c
ik
Binary variable indicating a time period on or after the start of the EOR operation in sink i.
Binary variable indicating a time period on or before the end of the EOR operation in sink i.
Time at which the operation starts (y)
ik
(
T
i
)start
2
. Problem Statement
The problem statement is as follows. The system consists of m depleted oil reservoirs and one CO
2
source. In this study, the CO
other hand, each reservoir is characterized by a capacity, C
injected, Y with a value, V . The injection to each reservoir should be within the bounds, (F
and a portion, defined as S , of the CO injected is sequestered. The amount of CO
amounting to C . The operations corresponding to these reservoirs have fixed durations ΔT
2 T
source operates for T years and has a fixed total flow rate of F . On the
i
and yields a fixed amount of oil per CO
2
max
min
i
i
i
)
i
to (F )
i
2
2
stored gets credits
and can start
i 2
and (T ) . The cost of transporting and injecting CO to the reservoir is estimated using a
*
i
early
late
between (T
linear cost function with a fixed component of (C
proportional to the flow rate to the reservoir and the distance of the reservoir to the source.
i
)
pp
pp
o
)
and variable component of (C) . The latter is

John Frederick D. Tapia et al. / Energy Procedia 61 (2014) 595 – 598
597
3
. Optimization Model
The objective function is to maximize the total profit from all EOR operations:
pp
pp
max Z = Σ
i
Σ
k
(fik)(Y
i
V
i
)(I
k
) + Σ
i
Σ
k
(fik)(S
i
)(C*)ꢀ Σ
i
[(C
o
) + (C) (f
i
D
i
)]
(Eq. 1)
Subject to:
a
c
a
ik + cik ꢀ 1= bik
ik ≤ ci(k ꢀ 1)
ik ≥ ai(k ꢀ 1)
׊i,k
׊i,k
׊i,k
׊i
׊i
׊i
׊k
׊i
׊i,k
׊i,k
׊i,k
׊i
(Eq. 2)
(Eq. 3)
(Eq. 4)
(Eq. 5)
(Eq. 6)
(Eq. 7)
(Eq. 8)
(Eq. 9)
(Eq. 10)
(Eq. 11)
(Eq. 12)
(Eq. 13)
(Eq. 14)
start
(
(
T
T
i
)
i
k ik
= T ꢀ Σ a
early
start
)late
)
≤ (T
(fik)(S ) ≤ C
(fik) ≤ F
)min ≤ f
i
)
≤ (T
i
Σ
Σ
k
i
i
i
T
(
f
f
f
F
i
i
≤ (F
i
)max
ik ≤ f
ik ≤ F
ik ≥ f
ik= ΔT
i
T
b
ik
i
ꢀ F
T
(1ꢀ bik
)
Σ
k
b
i
ꢀ
k
I
k
= (1+r)
׊i,k
The objective function has three components: the revenue for recovered oil, the credits for CO
sequestered and the cost of facilities for transporting CO to each reservoir. Equations 2, 3 and 4 show the
relationship of three binary variables used for scheduling the EOR operations. Equations 5 and 6 are used
for obtaining the time at which the operation is carried out. Equation 7 ensures that the total CO stored
during the operation is less than the capacity. Equation 8 is the flow rate balance and expresses the
distribution of available CO to each reservoir. Equation 9 to 12 ensures that the flow rate is within the
2
2
2
2
bounds and fixed at all time periods. Equation 13 links the span of EOR operations to binary decision
variables. The time of value of money is calculated by Equation 14.
4
. Case Study
The model is tested using a case study solved with Lingo 12.0 on a PC of 2.00 GHz and 4.00 Gb
RAM. Data for the case study are given in Table 1. The source is operating for 25 years in which the total
capturable CO is 8.5 Mt/y. An 8% interest rate is assumed for the entire time horizon. Transporting CO
2
2
to the reservoirs has a fixed cost of $20 million and a variable cost of $20,000/Mt/km. The optimal
solution is shown in Figure 1 About 10% of the total revenue is from the oil recovered. According to the
results, 53% of the total flow rate is allocated to Sink A, accounting for 27% of the total $124 million oil
revenue. From this case study, it is shown that no operations are performed after 16 years. Over the entire
2
planning horizon, 92 out of 117.5 Mt CO injected (78%) were stored in these sink. The optimization
model solves the problem of both the allocation of available CO
operations in this case study.
2
and scheduling of the three EOR
Figure 1 Optimal Solution for Case Study

5
98
John Frederick D. Tapia et al. / Energy Procedia 61 (2014) 595 – 598
Table 1. Case Study Data.
Sink A
1
15?
100
2
15
Sink B
2
10
100
2
15
Sink C
1
15?
100
2
early
Earliest Start of Operation, O
i
, y
late
Latest Start of Operation, O
Capacity, C , Mt
Minimum Flow Rate, F
i
, y
i
min
i
, Mt/y
, Mt/y
max
Maximum Flow Rate, F
i
15
Oil Yield, Yi, million bbls/Mt
Oil Value, Vi, $/bbl
0.009490
100
0.95
15
0.02847
70
0.85
10
0.04745
90
0.35
15
Sequestration Parameter, S
i
Operating Life for EOR, ΔT, y
Distance from the source (km)
100
300
600
5
. Conclusion and Future Works
A discrete time mixed integer linear program (MILP) for scheduling enhanced oil recovery (EOR)
operations with geological CO sequestration has been developed. The model allows the allocation of
CO from a single source to different reservoirs that corresponds to different EOR projects of fixed
durations. Future work includes extending the model to multiple sources of CO supply as well as
2
2
2
considering other aspects such as uncertainties in oil prices, reservoir storage capacity and oil yield.
Acknowledgements
We would like to acknowledge the Philippine Department of Science and Technology through its
ERDT program and the National Research Council of the Philippines through its Grant #500-192 for
financial support.
References
[
1] Turk, G. A. Cobb, T. B., Jankowski, D. J., Wolsky, A. M., Sparrow, F. T., 1987. CO2 transport: A new application of the
assignment problem. Energy 1987; 12: 123-130.
2] Tan, R. R., Aviso, K. B., Bandyopadhyay, S., Ng, D. K. S. Optimal source-sink matching in carbon capture and storage
systems with time, injection rate and capacity constraints. Environ. Prog. Sustain. Energy 2013; 32(2): 411-416.
3] Tan, R. R., Aviso, K. B., Bandyopadhyay, S., Ng, D. K. S. (2012) Continuous-Time Optimization Model for Source–Sink
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4] Lee, J. Y., Chen C.L. (2012) Comments on “Continuous-Time Optimization Model for Source-Sink Matching in Carbon
[
[
[
Capture and Storage Systems”. Ind. Eng. Chem. Res. 2012; 51(35): 11590–11591.
Biography
J.F.D. Tapia is a Master of Science (MS) in Chemical Engineering graduate in De La Salle University-
Manila, Philippines. His work includes developing optimization models such as linear program applied to
carbon capture and storage (CCS) system source-sink matching.