dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, for estimating liquid heat capacity, it is necessary to add a perfect-gas heat capacity term (c P,PG ) to the residual heat capacity. Therefore, an accurate correlation for the c P,PG T ð Þ function is also needed when accurate c sat P,liq data are available. Table  1  summarizes the data available in such a database. For more details, and in particular the temperature range on which the data were generated, the reader is referred to the paper by Piña-Martinez et al. 6  Table  2  presents a summary of the chemical families in which the 1800 compounds are distributed. In addition, Table  2  presents the distribution of NSA and SA compounds among the chemical families. As previously stated, the 1800 pure compounds are divided into 1252 NSA compounds and 548 SA compounds so that the molecule dataset contains 70% of NSA and 30% of SA compounds.
The process used to determine the associating character (NSA or SA) of a given molecule can be found in Piña-Martinez et al. 6  In short, the screening charge density distribution, sigma-profile (σ-profile), was generated for each compound. If the σ-profile presents peaks below a hydrogen-donor sigma-cutoff value and above a hydrogen-acceptor sigma-cutoff value, it is concluded that the molecule can form hydrogen bond (HB) with another similar molecule and thus tends to self-associate. The above-presented database was used by Piña-Martinez et al. 6  in order to assess the accuracy of four cubic equations of state (CEoS): original SRK, original Peng-Robinson (PR), tc-RK, and tc-PR where tc designates the translated and consistent 8-10 versions of such EoS. In this article, we seek to assess the performances of molecularbased EoS, in particular those belonging to the statistical associating fluid theory (SAFT)  11  family.
The second one is that in many cases, users of commercial process simulators can be led to represent self-associating compounds with a SAFT-type EoS without any association term due to a lack of a database containing the associating parameters (by default, they are set to zero). In summary, the performance of the PC-SAFT and the I-PC-SAFT EoS without an association term will be evaluated over a large number of non-associating and self-associating molecules by determining their capacity at correlating the following properties: P sat , v sat liq , Δ vap H, c sat P,liq , T c , P c , and v c.
| ASSESSING THE ACCURACY OF THE PC-SAFT AND I-PC-SAFT EoS: RESULTS AND DISCUSSION In this section, the performances of PC-SAFT and I-PC-SAFT are evaluated by assessing their accuracy over the 306,700 data points relative to the 1800 pure compounds contained in the reference database proposed by Piña-Martinez et al. 6  Deviations are expressed in terms of mean absolute percentage errors (MAPEs) defined by Equation (3) as the average of the absolute percentage deviations between experimental and calculated properties over   3  for the PC-SAFT and I-PC-SAFT EoS. The detail of deviations for each molecule are reported in Appendix A2. For possible comparison, the MAPEs obtained with the tc-PR EoS, which was found to be the most accurate of the four cubic equations of state evaluated against the same database in our previous article  6  are recalled.
In accordance with Moine et al. ,  14  it is believed that the embedding of three component-dependent parameters in an EoS is not enough to describe accurately all the properties of a pure component. The PC-SAFT and tc-PR EoS have similar accuracy. The PC-SAFT EoS, however predicts better liquid molar volumes, whereas the tc-PR EoS takes the lead for the correlation of the critical coordinates (T c and P c ) and the energetic properties (Δ vap H and c sat P,liq ). To answer a question raised by one of the reviewers of this article, speed of sound (w) and Joule-Thomson coefficients (μ JT ) data were not included in the proposed database simply because such data are only available for a restraint number of pure components. What can be said about their correlation? It is believed that SAFT-type EoS should correlate such data more accurately than CEoS because the parametrization of CEoS, that gives priority to the reproduction of critical coordinates and vapor pressure data, entails a non-satisfactorily reproduction of the isothermal compressibility (κ T ) and the isobaric volume expansivity (α P ) that are needed to calculate w and μ JT through the following equation: w ¼ v ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c P =c v M κ T v ð Þ s with: κ T v ¼ À ∂v ∂P À Á T μ JT ¼ v α P T À 1 ð Þ c P with: α P ¼ 1 v ∂v ∂T P 8 > > > > < > > > > : : ð4Þ Due to their parametrization that traditionally involves liquid-density data, SAFT EoS are less concerned with this shortcoming.
For possible comparison, results obtained with the tc-PR EoS, as published in our previous paper,  6  are recalled in Figure  3. A major feature is the huge similitude between SAFT-type EoS and the tc-PR EoS. Indeed, regardless of the considered EoS (PC-SAFT, I-PC-SAFT, or tc-PR), about 75% of the pure components are well-represented, according to the criterion defined in Equation (5)   (see the left panel in each figure). As reported in Table  2 , the database contains 70% of NSA fluids and 30% of SA components. In the absence of an association term, it could be believed that the well-modeled molecules are essentially NSA fluids. This is however not the case at all. Indeed, as previously reported in our article devoted to CEoS, almost 65% of the SA molecules are well-modeled (see the right panel in Figures  1 2 3 ), while almost 17% of the NSA compounds are badly-modeled (refer to the middle panel in Figures  1 2 3 ).
The assessment was performed over 1800 molecules that have accurate P sat and ρ sat liq correlations in the DIPPR databank making possible to generate accurate pseudo-experimental data. Among the 1800 fluids, 1536 also possess an accurate correlation for Δ vap H and 890 for c sat P,liq making possible to generate 50 data points for such properties. The critical properties (T c , P c , and v c ) are available for the 1800 components so that in the end, the resulting database contains 306,700 high-quality pseudo-experimental points. These 1800 molecules were divided into 1252 non-self-associating (NSA) compounds and 548 self-associating (SA) compounds. F I G U R E.
| CONCLUSION Results obtained over the entire database highlight: • The very high accuracy of both SAFT-type EoS that got an average overall deviation of 2. 2% and 3. 6% over the 306,700 experimental data points respectively. Deviations on the derived properties (Δ vap H and c sat P,liq ) are around 4% which is higher than those observed for vapor pressures and liquid densities. For possible comparison, the tc-PR EoS, which was found to be the most accurate cubic EoS got an average deviation of 1. 9% over the entire database. • The strong influence of the parameterization: the PC-SAFT EoS is parameterized in order to minimize the deviations between experimental and calculated P sat and v sat liq data so that such properties are perfectly correlated. The counterpart is a large deviation on the critical pressure.
This second result is even less expected since no association term was included in the considered equations of sate. Such results do not help to decide in which direction efforts have to be devoted to improve SAFT-type and cubic EoS that do not embed an association term. Is the addition of an association term really the most suited solution? Let us also recall that this article is the first one in which molecular parameters suitable for the PC-SAFT and I-PC-SAFT EoS are published for 1800 pure compounds. Such parameters can be used immediately in any commercial process simulator. Finally, the authors hope that the database used in this article and also in a previous one dedicated to cubic EoS will be widely adopted by the scientific community to identify the strengths and weaknesses of a given thermodynamic model to correlate (or predict) the properties of pure compounds.
6  A B L E 1 Overview of the data available in the database proposed by Piña-Martinez et al. 6 Number of pure components for which the property Property is available Number of data points P sat 1800 90,000 (50 per component) v sat liq 1800 90,000 (50 per component) Δ vap H 1536 76,800 (50 per component) c sat P,liq a 890 44,500 (50 per component) T c 1800 1800 P c 1800 1800 v c 1800 1800 Total 306,700 a An equivalent number of c P,PG data is available in order to be able to calculate c sat P,liq from an EoS.
6 ist of the chemical families and corresponding distribution of the 1800 pure compounds available in the database proposed by Piña-Martinez et al. 6 T A B L E 2 Chemical family Number of compounds Proportion of compounds (%) Number of NSA compounds Number of SA compounds Alcohols 131 7. 3% 4 127 Alkanes 163 9. 1% 163 0 Alkenes 122 6. 8% 122 0 Alkynes 18 1. 0% 18 0 Amines/amides 102 5. 7% 0 102 Aromatics 158 8. 8% 158 0 Epoxides 18 1. 0% 16 2 Esters/ethers 238 13. 2% 203 35 Halogen compounds 232 12. 9% 197 35 Inorganic compounds 70 3. 9% 61 9 Ketones/aldehydes 80 4. 4% 78 2 Nitriles 31 1. 7% 9 22 Nitrogen compounds 99 5. 5% 64 35 Organic acids 82 4. 6% 0 82 Organic salts 13 0. 7% 11 2 Other compounds 9 0. 5% 3 6 Peroxides 11 0. 6% 3 8 Polyfunctional compounds 68 3. 8% 14 54 Silicon compounds 61 3.
9% 4. 78% 1. 7% EoS,NSA X and MAPE EoS,NSA X denote the mean standard deviation (SD) and MAPE for property X calculated with a given EoS over the 1252 NSA components of the database (see Table7for the numerical values). If one or both conditions of Equation (5) are not satisfied, the pure component is considered as badly-modeled. Results are T A B L E 5.
Sensitivity of the DFB diode laser-based CRDS system Due to the short lifetime of HO 2 (no commercial product, it needs to be generated on-site), it is difficult to obtain very stable concentrations. Time series measurement of the zero air was performed to investigate the stability and baseline precision of the spectrometer. The result is shown in Fig. 5. The sampling time for each data point was about 0. 0067 s (single ring-down event without averaging). The minimum detectable absorption coefficient (α min ) can be expressed as: α min = ∆τ min τ 0 1 cτ 0 (4) The averaged value of τ 0 was 136. 3 µs with a standard deviation of 0. 21 µs. The time series measured α min was about 3. 7 × 10 -10 cm -1. Allan variance analysis shows that the sensitivity was improved to 3. 0 × 10 -11 cm -1 with 1 s data acquisition time (averaging result of 150 ring-down events).
Methodological Details We adopt a simple model with methanol as a co-initiator, TMC as a cyclic carbonate, and TBD and DMAP as organocatalysts. All calculations in this study were conducted with the Gaussian 16 program package  [29]. Geometries of the chemical relevant species were obtained by DFT from optimizations using the hybrid meta-GGA exchange-correlation functional M06-2X  [30]  together with the 6-31+G(d,p) basis set. The choice of this functional was determined by its broad applicability and remarkable performance in many areas of chemistry including thermochemistry and reaction kinetics, providing reliable energies and barrier heights while considering midrange noncovalent interactions  [30] [31] [32]. In all calculations, the solvent effects were considered by using the SMD continuum solvation model  [33]  with the parameters of dichloromethane.
Acknowledgments: JPPR is grateful to the  Fundação para a Ciência e a Tecnologia (FCT)  for funding LAQV through projects  UIDB/50006/2020  and  UIDP/50006/2020. The authors are grateful to  Aurélie Malfait  for SEC measurements. The authors also acknowledge financial support from the  Initiatives for Science, Innovation, Territories and Economy (I-SITE)   Lille Nord-Europe (MLT PhD fellowship ). Université de Lille, Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation ,  Région Hauts-de-France  are also acknowledged for supporting and funding partially this work.
Acknowledgements We acknowledge  Scientific Centre for Optical and Electron Microscopy (ScopeM, ETH Zürich)  for providing access to electron microscopy facilities. Z. C. thanks  ETH Zürich  for financial support ( ETH-40 17-2 ). S. R. D. and C. C. acknowledge the  Swiss National Science Foundation  (grants  200021_169134 ,  200020B_192050 , and  CRSII5_183495 ). The Swiss Norwegian Beamlines (SNBL) at  European Synchrotron Facilities (ESRF)  is acknowledged for providing access to the BM31 beamline. This publication was created as part of  NCCR Catalysis  (grant number  180544 ), a  National Centre of Competence in Research  funded by the  Swiss National Science Foundation.
X-ray diffraction (XRD) measurements were recorded with a Panalytical Empyrean diffractometer for the bare cenospheres, so-synthesized, reduced and post-run catalysts. The diffractometer working in Bragg-Brentano q-q geometry was equipped with Cu Ka (l ¼ 1. 5406 A) radiation. The collected data was within a 2q range of 10e90 deg. The average diameter of the Ni crystallite was calculated via the Scherrer's equation, considering the instrument broadening correction, and the shape factor of 0. 89  [34]. Low-temperature N 2 sorption measurements were carried out with the Belsorp Mini II (BEL Japan). Prior to the measurement, catalysts were outgassed for 2 h at 300 C. Temperature-programmed reduction (H 2 -TPR) and CO 2 -temperature-programmed desorption (CO 2 -TPD) measurements were performed with the BELCAT-M apparatus, equipped with a TCD detector.
Catalytic tests were carried out at a temperature range from 250 C to 450 C, with the catalyst kept in steadystate operation for 30 min at each of the studied temperatures, with a heating rate between steps of 10 C/min. Equilibrium CO 2 conversion and selectivity to CH 4 were calculated with HSC Chemistry 5. 0 software. The conversion of CO 2 and the selectivity to CH 4 were calculated based on Eqs. (  2 ) and (  3 ) respectively. CO 2 conversion c CO 2 ¼ F CO 2 inlet À F CO 2 outlet F CO 2 inlet Eq. 2 CH 4 selectivity c CH 4 ¼ F CH4outlet F COoutlet þ F CH4outlet Eq. 3 where F is the flow calculated from the concentration of the gases.
Acknowledgments Paulina Summa  would like to acknowledge  French Embassy in Poland  for providing the scholarship. Bogdan Samojeden  and  Monika Motak  gratefully acknowledge the financial support from the Grant AGH No. 16. 16. 210. 476. SEM & EDS were funded by  Sorbonne Universit e, CNRS  and  R egion Ile de France , and are part of FCMat, The  Federation of Chemistry and Materials of Paris-Center.
The optimality of the solution will be judged based on a unique or a set of KPI defined by the user. Next, step 2 is assigned to identify the mass and energy transformations required to translate raw materials into desired products. These transformations include any physical and chemical change in the initial and intermediate states of the process, such as pressure increase or decrease, heat exchanges, chemical reactions, etc. Data from the literature and the previous knowledge of the process are useful for these identifications, as they allow to announce process constraints in terms of operating condition ranges. The use of a process simulation software limits the search of the optimal unit arrangement to the predefined models of equipment. The equipment to be evaluated is selected according to the process needs, as commonly technological or economic limitations exists.
Optimization algorithm Solving process engineering superstructures requires to consider continuous and discrete variables using Mixed-Integer Non-Linear Programming (MINLP) algorithms  [32]. For this work, MIDACO-Solver is used, as it has proven to be useful for its integration to ProSimPlus software for other applications of superstructure optimization  [29, 31]. This algorithm is built as a general-purpose solver for mono-and multi-objective optimization problems, acting as a black box capable of handling nonexplicit objective functions, with or without equality or inequality constraints, as well as being able to handle critical properties such as non-linearities or discontinuities. It solves multi-objective MINLP with the principles of an extended evolutionary Ant Colony Optimization (ACO), based on the gaussian probability density functions and the generation of ants/individuals  [33].
Process considerations and hypothesis The models used for each equipment in the process flowsheet simulation are described hereafter. The process is simulated using the Predictive Peng Robinson (PPR78) thermodynamic model  [47] , available in ProSimPlus thermodynamic database, which allows to predict the binary interaction parameters between compounds. Simple compressors are considered to have isentropic, mechanic, and electrical efficiencies of 75 %, 95 % and 100 %, respectively. For the compression zone, temperature fixed by the intercoolers is equal to 25 °C. A temperature limit can be fixed at the outlet stream of the compressors, to avoid dangerous levels. The outlet temperature of the compressors can exceed 200 °C in cases where 2 compressors are used at pressures higher than 150 bar.
Cost estimates The economic evaluation is based on the calculation of expenditures of the project, using as reference the Manual of Process Economic Evaluation  [48] , and more specifically, the Pré-Estime method. This method is embedded in ProSimPlus and eases the economic estimation. The investment costs consider each equipment as the sum of parts for which a base price is calculated, based on technological and sizing parameters, as well as operating conditions. For each equipment, a specific correlation exists, which is multiplied by correction, assembly, and sizing factors to obtain the assembled real price of each module. Correlations and factors for capital expenditures calculations are available in the Supplementary Materials. Operational expenditures include maintenance costs, materials consumption, miscellaneous, among others.
Cyclic voltammetry and scanning electrochemical microscopy Electrochemical measurements were carried out on a Biologic VMP3 potentiostat operating under EC-Lab V10. 38 software. Cyclic voltammetry experiments were conducted using a symmetric Swageloks-type two-electrode cell with PILs@CNT or redox-PILs@ CNT material at both positive and negative electrodes. A glass microfiber Whatman filter paper (thickness 260 µm and pore diameter 1. 6 µm) was utilized as the separator. Cyclic voltammetry was performed in a potential window of 2. 8 V with scan rates from 5 mV/s to 200 mV/s.
Turbine aerodynamic optimization methods Design optimization is increasingly deployed to improve the performance of radial turbines. The approach of Mueller et al. [37]  had similar objectives to the present work, viz. to optimize a turbocharger radial turbine for high efficiency and low inertia, whilst meeting structural constraints. The authors performed optimization using a differential evolutionary algorithm, an artificial neural network (ANN), and a database. The method is considered multidisciplinary since ANN predictions were validated against RANS (Reynolds-averaged Navier--Stokes) CFD (Computational Fluid Dynamics) as well as centrifugal stress simulations. Schwalbach et al. [38]  also combine RANS CFD and FEM (Finite Element Method) tools for multidisciplinary optimization of radial turbine efficiency under mechanical stress constraints, but inside an adjoint-based optimization framework.
The wastegate controls boost pressure by regulating the turbine wheel by-pass flow area and thereby the turbine power developed. Measured compressor and turbine maps (i. e. , flow and efficiency characteristics) for this production turbocharger, provided by MTEE, are loaded into the engine model to allow its performance to be derived during simulations. Indeed, this has become an essential part of the typical turbocharger matching process in the automotive industry. Validation of the turbocharged engine model against dynamometer test data was carried out over a set of full-and part-load engine operating points. Fig. 1  compares simulated engine performance against test data for both full-and part-load operation. All values are normalized with respect to the maximum value observed in the test.
Finally, a 3. 3% greater inertia was calculated by CAD software for the 3D turbine wheel,   compared to the NN prediction. To evaluate the effect of these small variations between the meanline and 3D descriptions, the CFD-predicted performance maps and the revised CAD-calculated inertia value were used to re-run engine simulations. The consequent difference in transient response is shown in Fig. 21. Although the 3D design has slightly different performance characteristics and slightly greater inertia, the change in transient performance is quite small. In fact, the 3D design shows about a 2% better response. The impact of the small inertia increase appears to be more than mitigated by the greater turbine efficiency predicted by CFD at higher rotational speeds, compared to the meanline estimations.
FEA simulations A preliminary analysis using the commercial FEA software SIMULIA Abaqus was conducted to determine if the stresses in the MOT2-SM1 design exceeded accepted limits. The solid model of the wheel is first discretized into small geometric elements, and the equations accounting for the forces acting on the wheel and the resulting displacement are solved using an inversion matrix. The effects of pressure and temperature loading are much smaller than the centrifugal stresses  [50]  so are not included in the FEA simulations. Fig. 27  shows the corresponding tetrahedral mesh of 0. 4 million elements, created using the inbuilt generator in SIMULIA Abaqus. FEA simulations at the 100% design speed were performed for both the baseline and MOT2-SM1 turbine wheels in order to model maximum centrifugal loading.
Experimental validation To validate the turbine optimization process, gas stand testing of both the optimized and baseline turbine designs was performed. As well as the optimized MOT2-SM2 design, fresh samples of the baseline turbine and volute were machined at the same time to ensure as fair a backto-back comparison as possible, with all test pieces having been manufactured by the same process and to the same level of quality (e. g. , dimensional tolerances and surface finish). Figs. 30 and 31 validate the meanline and CFD predictions for the optimized MOT2-SM2 design against experimental mass flow parameter and total-static efficiency measurements, respectively, for three rotational speeds. A reminder that the data is normalized with respect to the peak values of the corresponding parameter.
) represent clusters of points from a much larger set of 80+ engine operating points (not shown) that were measured by the manufacturer under the Worldwide harmonized Light vehicles Test Procedure (WLTP), specifically the Worldwide harmonized Light vehicles Test Cycle (WLTC) for Class 3b vehicles [65]. The clustering approach first removes operating points below 4 bar BMEP, since at these operating points the turbocharger turbine has limited influence because the wastegate is open. The remaining points are then classified by the proportion of the total WLTP fuel consumption they each represent. At each engine speed in the dataset, a grouping is made for those points representing ⩾1. 4% of total WLTP fuel.
The code was written in python, and it uses the open-source libraries matplotlib, pandas, NumPy, and SciPy for data visualization, handling, and fitting. PyQt is the library used for the graphical elements because it offers excellent flexibility and compatibility with different operating systems. Our software can analyze eight samples simultaneously that share the same time data, shortening the analysis process to a couple of minutes. Its source code is available at Github. This article describes its main features, the workflow, and we present three examples for data analysis whose data tables are available for user testing.