Liquid Density Prediction of Ethanol/Water, Using Artificial Neural Network

In this work, our objective was to get a reliable model for predicting liquid density ethanol-water and use it again later in modeling the ethanol production process from biomass. Hence, the unreliability of the Peng-Robinson equation of state to predict this property was shown. The average absolute deviation of this prediction is equal to 14.72 %. To have a reliable model, an artificial neural network (ANN) method was followed. Levenberg–Marquardt algorithm is used to choose the optimized ANN structure that has ten neurons in the hidden layer, three neurons in the input layer, and one neuron in the output layer, with a tangent-sigmoid and linear transfer functions, in the hidden and the output layers, respectively. The model training was done using 348 experimental data points from published experiments, realized at different liquid mole fraction range, pressure (0.10 to 10.00MP), and temperature (298.15 K to 476.2 K). The correlation coefficient between the experimental and liquid phase density was 0.9999 for training, validation, and testing the model. Statistical analysis is employed to evaluate the accuracy of the ANN, showing that the average absolute deviation, root mean square, and the Bias are 0.047 %, 0.003 %, and -0.004 %, respectively. So the ANN model gives a good estimation of liquid density, for mixture ethanol/water, with a relative importance of pressure, composition, and temperature equal to 41%, 34 %, and 25 %, respectively.

High precision evaluation of the combustion enthalpy by ab-intio computations

Accurate evaluation of combustion enthalpy is of high scientific and industrial importance. Although via ab-initio computation of heat of reactions, as one of the promising and well-established approaches in computational chemistry, this goal should in principle be achievable, examples of reliable and precise evaluation of heat of combustion by ab-initio methods has surprisingly not yet been reported. A handful of works carried out for this purpose report significant inconsistencies between the ab-initio evaluated and experimentally determined combustion enthalpies and suggest empirical corrections to improve the accuracy of predicted data. With this background, the main aims of the present study is to investigate the reasons behind those reported inconsistencies and propose guidelines for highly accurate evaluation of combustion enthalpy via ab-initio computations. Through the provided guidelines, the most accurate results ever reported, with average absolute deviation, mean unsigned error and correlation coefficient of 1.556 kJ/mole, 0.072% and 0.99999, respectively, is achieved for theoretically computed combustion enthalpies of 40 studied hydrocarbons.

Density, Enthalpy of Vaporization and Local Structure of Neat N-Alkane Liquids

The properties of alkanes are consequential for understanding many chemical processes in nature and industry. We use molecular dynamics simulations with the Amber force field GAFF2 to examine the structure of pure liquids at each respective normal boiling point, spanning the 15 n-alkanes from methane to pentadecane. The densities predicted from the simulations are found to agree well with reported experimental values, with an average deviation of 1.9%. The enthalpies of vaporization have an average absolute deviation from experiment of 10.4%. Radial distribution functions show that short alkanes have distinct local structures that are found to converge with each other with increasing chain length. This provides a unique perspective on trends in the n-alkane series and will be useful for interpreting similarities and differences in the n-alkane series as well as the breakdown of ideal solution behavior in mixtures of these molecules.

Simulation of Organic Liquid Products Deoxygenation by Multistage Countercurrent Absorber/Stripping Using CO2 as Solvent with Aspen-HYSYS: Thermodynamic Data Basis and EOS Modeling

In this work, the thermodynamic data basis and equation of state (EOS) modeling necessary to simulate the fractionation of organic liquid products (OLP), a liquid reaction product obtained by thermal catalytic cracking of palm oil at 450 °C, 1.0 atmosphere, with 10% (wt.) Na2CO3 as catalyst, in multistage countercurrent absorber/stripping columns using supercritical carbon dioxide (SC-CO2) as solvent, with Aspen-HYSYS was systematically investigated. The chemical composition of OLP was used to predict the density (ρ), boiling temperature (Tb), critical temperature (Tc), critical pressure (Pc), critical volume (Vc), and acentric factor (ω) of all the compounds present in OLP by applying the group contribution methods of Marrero-Gani, Han-Peng, Marrero-Pardillo, Constantinou-Gani, Joback and Reid, and Vetere. The RK-Aspen EOS used as thermodynamic fluid package, applied to correlate the experimental phase equilibrium data of binary systems OLP-i/CO2 available in the literature. The group contribution methods selected based on the lowest relative average deviation by computing Tb, Tc, Pc, Vc, and ω. For n-alkanes, the method of Marrero-Gani selected for the prediction of Tc, Pc and Vc, and that of Han-Peng for ω. For alkenes, the method of Marrero-Gani selected for the prediction of Tb and Tc, Marrero-Pardillo for Pc and Vc, and Han-Peng for ω. For unsubstituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb, Marrero-Gani for Tc, Joback for Pc and Vc, and the undirected method of Vetere for ω. For substituted cyclic hydrocarbons, the method of Constantinou-Gani selected for the prediction of Tb and Pc, Marrero-Gani for Tc and Vc, and the undirected method of Vetere for ω. For aromatic hydrocarbon, the method of Joback selected for the prediction of Tb, Constantinou-Gani for Tc and Vc, Marrero-Gani for Pc, and the undirected method of Vetere for ω. The regressions show that RK-Aspen EOS was able to describe the experimental phase equilibrium data for all the binary pairs undecane-CO2, tetradecane-CO2, pentadecane-CO2, hexadecane-CO2, octadecane-CO2, palmitic acid-CO2, and oleic acid-CO2, showing average absolute deviation for the liquid phase (AADx) between 0.8% and 1.25% and average absolute deviation for the gaseous phase (AADy) between 0.01% to 0.66%.

Audio Enhancement of Physical Models of Musical Instruments Using Optimal Correction Factors: The Recorder Case

A simulation of a musical instrument is considered to be a successful one when there is a good resemblance between the model’s synthesized sound and the real instrument’s sound. In this work, we propose the integration of physical modeling (PM) methods with an optimization process to regulate a generated digital signal. Its goal is to find a new set of values of the PM’s parameters’ that would lead to a synthesized signal matching as much as possible to reference signals corresponding to the physical musical instrument. The reference signals can be: (a) described by their acoustic characteristics (e.g., fundamental frequencies, inharmonicity, etc.) and/or (b) the signals themselves (e.g., impedances, recordings, etc.). We put this method into practice for a commercial recorder, simulated using the digital waveguides’ PM technique. The reference signals, in our case, are the recorded signals of the physical instrument. The degree of similarity between the synthesized (PM) and the recorded signal (musical instrument) is calculated by the signals’ linear cross-correlation. Our results show that the adoption of the optimization process resulted in more realistic synthesized signals by (a) enhancing the degree of similarity between the synthesized and the recorded signal (the average absolute Pearson Correlation Coefficient increased from 0.13 to 0.67), (b) resolving mistuning issues (the average absolute deviation of the synthesized from the recorded signals’ pitches reduced from 40 cents to the non-noticeable level of 2 cents) and (c) similar sound color characteristics and matched overtones (the average absolute deviation of the synthesized from the recorded signals’ first five partials reduced from 41 cents to 2 cents).

Around-the-Head Tactile System for Supporting Micro Navigation of People with Visual Impairments

Tactile patterns are a means to convey navigation instructions to pedestrians and are especially helpful for people with visual impairments. This article presents a concept to provide precise micro-navigation instructions through a tactile around-the-head display. Our system presents four tactile patterns for fundamental navigation instructions in conjunction with continuous directional guidance. We followed an iterative, user-centric approach to design the patterns for the fundamental navigation instructions, combined them with a continuous directional guidance stimulus, and tested our system with 13 sighted (blindfolded) and 2 blind participants in an obstacle course, including stairs. We optimized the patterns and validated the final prototype with another five blind participants in a follow-up study. The system steered our participants successfully with a 5.7 cm average absolute deviation from the optimal path. Our guidance is only a little less precise than the usual shoulder wobbling during normal walking and an order of magnitude more precise than previous tactile navigation systems. Our system allows various new use cases of micro-navigation for people with visual impairments, e.g., preventing collisions on a sidewalk or as an anti-veering tool. It also has applications in other areas, such as personnel working in low-vision environments (e.g., firefighters).

Neural network method for climate forecasting water content of the Chernorechensk reservoir

New method for precipitation forecasting at the Ai-Petri region is proposed in this work. This method includes a model based on artificial neural networks. A set of global oceanic and meteorological indices were used as the input parameters of the model. SST and SLP data sets from NCEP / NCAR and HadISST re-analyses in 1950-2020 were used for indices calculation. A feature of the proposed model is the decomposition of the predicted series into two orthogonal signals, their independent modeling and subsequent addition of the calculated model values. The sum of model calculations signals was verified. The model was verified in the period 2007–2020. The possibility of forecasting average monthly precipitation amounts with a lead time of up to 6 months is shown. It is found the possibility of the model to predict precipitation in the winter and summer seasons, September and October, which is 70% of the average long-term annual precipitation. It is shown that the best forecast of precipitation for the winter season can be made in November, and with a higher quality of the forecast – in December. The average absolute deviation in the control sample was 28% and 23%, respectively. Taking into account that the maximum precipitation in the mountains occurs in the cold half of the year, and the fact that the main volume of water content of the Chernorechensk reservoir is formed in the cold period of the year, then the forecast of precipitation for the winter season is of the greatest importance. The forecast of precipitation for the summer period and September-October can be made in April (the average absolute deviation is 22%). In addition, the work tested and confirmed the possibility of a climate forecast of atmospheric pressure. The obtained results can be useful for early assessment of the level of filling of the Chernorechensk reservoir.

Effect of PET-CT misalignment on the quantitative accuracy of cardiac 15O-water PET

Background Quantification of myocardial blood flow (MBF) with PET requires accurate attenuation correction, which is performed using a separate CT. Misalignment between PET and CT scans has been reported to be a common problem. The purpose of the present study was to assess the effect of PET CT misalignment on the quantitative accuracy of cardiac 15O-water PET. Methods Ten clinical patients referred for evaluation of ischemia and assessment of MBF with 15O-water were included in the study. Eleven different misalignments between PET and CT were induced in 6 different directions with 10 and 20 mm amplitudes: caudal (+Z), cranial (− Z), lateral (±X), anterior (+Y), and anterior combined with cranial (+ Y and − Z). Blood flow was quantified from rates of washout (MBF) and uptake (transmural MBF, MBFt) for the whole left ventricle and the three coronary territories. The results from all misalignments were compared to the original scan without misalignment. Results MBF was only minorly affected by misalignments, but larger effects were seen in MBFt. On the global level, average absolute deviation across all misalignments for MBF was 1.7% ± 1.4% and for MBFt 5.4% ± 3.2 Largest deviation for MBF was − 4.8% ± 5.8% (LCX, X + 20) and for MBFt − 19.3% ± 9.6% (LCX, X + 20). In general, larger effects were seen in LAD and LCX compared to in RCA. Conclusion The quantitative accuracy of MBF from 15O-water PET, based on the washout of the tracer, is only to a minor extent affected by misalignment between PET and CT.

Experimental measurements and modelling of viscosity and density of calcium and potassium chlorides ternary solutions

Measured viscosity and density data for ternary aqueous solutions of CaCl2 and KCl are presented at temperatures between 293 and 323 K with 5 K increment. A modified Jones–Dole was introduced by adding extra terms and proved to be suitable for modelling of the viscosity data. Goldsack and Franchetto, Hu and Exponential models are used to correlate the viscosity data, too. Al models are correlated as a function of temperature and concentration. All models had successfully predicted the viscosity with high precision reaching a maximum average absolute deviation (AAD) of less than 2.3%. The modified Jones–Dole showed the best results among other models. Viscosity of the ternary solution is higher than the viscosity of water by about 15% at low concentrations and reaches about 270% at the highest concentrations. The amount of CaCl2 has more significant effect on the ternary mixture viscosity compared to KCl. This has created difficulty in measuring the viscosity and consequently the challenge in finding the different models parameters. Ternary solution densities were successfully correlate with Kumar’s model with AAD of less than 0.4%. Comparison of the ternary solution density and viscosity with the few available data literature showed a good agreement.

APLIKASI HUKUM BENFORD DALAM MENGANALISA KASUS GARUDA INDONESIA

<p><strong>ABSTRACT</strong>: Benford's Law is a simple and effective tool in finding fraud for an auditor. The purpose of this study was to determine whether Benford's Law can be used effectively in analyzing the indication of ambiguity in the financial statements of PT. Garuda Indonesia (Persero) Tbk. 2018. To answer this question, this research uses all accounts in the company's financial statements from 2018 and 2017. In assessing whether these accounts have differences in distribution with Benford's Law, this study uses the average absolute deviation. Then the predicted absolute deviation results are tested by referring to the exposure of Garuda's incidental publications (July, 2019). The results of this study indicate that the value of the financial statements of PT. Garuda Indonesia (Persero) Tbk. in the year 2018 fulfills the Benford Law distribution pattern. Meanwhile, in 2017 there were several deviant account values which were then asked to be restated in the 2018 reporting. Benford's law has once again proven to be used as an effective tool in audit planning.</p><p><strong>Keywords:</strong> Benford Law, Garuda Indonesia Financial Statements, Average Absolute Deviation</p><p align="center"><strong> </strong></p><p><strong>ABSTRAK:</strong> Hukum Benford merupakan alat bantu yang sederhana dan efektif dalam menemukan kecurangan bagi seorang auditor. Tujuan dari penelitian ini adalah untuk mengetahui apakah Hukum Benford dapat digunakan secara efektif dalam menganalisa adanya indikasi kerancuan Laporan Keuangan PT. <a href="https://www.cnnindonesia.com/tag/garuda-indonesia">Garuda Indonesia</a> (Persero) Tbk. Tahun 2018. Untuk menjawab pertanyaan tersebut, riset ini menggunakan seluruh akun dalam laporan keuangan perusahaan tahun 2018 dan 2017. Dalam menilai apakah akun-akun tersebut memiliki perbedaan distribusi dengan Hukum Benford, penelitian ini menggunakan rata-rata deviasi absolut. Kemudian hasil rata-rata deviasi absolut diuji prediksinya dengan merujuk pada paparan publikasi insidentil Garuda (Juli, 2019). Hasil dari penelitian ini menunjukkan bahwa nilai akun Laporan Keuangan PT. <a href="https://www.cnnindonesia.com/tag/garuda-indonesia">Garuda Indonesia</a> (Persero) Tbk. Tahun 2018 memenuhi pola distribusi Hukum Benford. Sedangkan pada tahun 2017 terdapat beberapa nilai akun yang menyimpang dan kemudian diminta untuk disajikan ulang di pelaporan tahun 2018. Hukum Benford sekali lagi terbukti dapat digunakan sebagai alat bantu yang efektif dalam perencanaan audit.</p><p><strong>Kata Kunci: </strong>Hukum Benford, Laporan Keuangan <a href="https://www.cnnindonesia.com/tag/garuda-indonesia">Garuda Indonesia</a>, Rata-rata Deviasi Absolut</p>