Giovanni Di Nicola

A new equation for the thermal conductivity of organic compounds

This work presents a wide literature survey of the available data of the experimental thermal conductivity data of organic liquids. The experimental data were collected for 136 compounds belonging to the following families: refrigerants, alkanes, alkenes, aromatics, cycloalkanes, cycloalkenes, ethers, esters, ketones, carboxylic acids, and alcohols. The experimental data were regressed with the most reliable semi-empirical correlating methods existing in the literature and a reliable set of 4,584 experimental data was finally selected. The influence of several physical parameters on the thermal conductivity calculation is discussed and a new equation to represent the thermal conductivity of organic liquids at atmospheric pressure for temperatures below normal boiling point and at saturation for temperatures above the normal boiling point is presented. To minimize the deviation between the predictions and the experimental data and to find the optimal coefficients for the proposed equation, a statistical analysis was performed. The resulting equation is simple and is able to predict the thermal conductivities with low deviations for the major part of the collected data for the studied families.

Triple point measurements for alternative refrigerants

Triple point data are important in the refrigerating industry, defining the lowest temperature limit at which a refrigerant may circulate in a fluid state. For several refrigerants, triple point data present in the literature are extremely scarce or inaccurate. A recently developed Solid–Liquid Equilibria (SLE) apparatus was used to measure the triple point temperature of 16 of the most widely applied alternative refrigerants, namely three methane derivatives (fluoromethane, R41; difluoromethane, R32; trifluoromethane, R23), four ethane derivatives (pentafluoroethane, R125; 1,1,1,2-tetrafluoroethane, R134a; 1,1,1-trifluoroethane, R143a; 1,1-difluoroethane, R152a), five propane derivatives [1,1,1,2,3,3,3-heptafluoropropane, R227ea; 1,1,2,3,3,3-hexafluoropropane, R236ea; 1,1,1,3,3-Pentafluoropropane, R245fa; 1,1,2,2,3-pentafluoropropane, R245ca; 1,1,1,3,3,3-hexafluoropropane, R236fa), and four hydrofluoro-olefines (2,3,3,3-tetrafluoroprop-1-ene, R1234yf; trans-1,3,3,3 tetrafluoropropene, R1234ze(E); 3,3,3-trifluoropropene, R1243zf; 1,2,3,3,3-pentafluoropropene, 1225ye(Z)]. The experimental setup, that was recently adopted for the SLE estimation of binary systems containing carbon dioxide (J Therm Anal Calorim 105:489–493, 2011), comprises a measuring cell and a system for drawing the liquid nitrogen directly from its insulated tank with the aid of compressed air: the carrier fluid circulating in the circuit is thus the refrigerant itself. The measurements were performed both in the heating and in the cooling mode. In order to confirm the functional efficiency and fine adjustment of the apparatus, the already available triple point literature data for carbon dioxide, dimethyl ether, and nitrous oxide were also compared with the ones measured by the present setup, confirming the validity of the setup. The measured triple point data for the refrigerants revealed generally good agreement with the literature, excepting a few fluids that revealed some discrepancies.

A new scaled equation to calculate the surface tension of ketones

This work presents a new formula to calculate the surface tension of ketones. As a first step, an analysis of the available data of the experimental surface tension data for ketones was made. Experimental data were collected for the following pure fluids: acetone, 2-butanone, 2-pentanone, 3-pentanone, 2-hexanone, 3-heptanone, 4-heptanone, 2-octanone, and 6-undecanone. The data were then regressed with the most reliable semi-empirical correlation methods in the literature based on the corresponding states theory. The final equation proposed is very simple and gives noticeable improvement with respect to existing equations.

Bioenergy II: Modeling and Multi-Objective Optimization of Different Biodiesel Production Processes

One of the most promising renewable fuels proposed as an alternative to fossil fuels is biodiesel. The competitive potential of biodiesel is limited by the price of vegetable oils, which strongly influences the final price of biofuels. An appropriate planning and design of the whole production process, from the seed to the biodiesel end product, is essential in order to contain the fallout of energy inefficiencies in the high price of the end product. This study focuses on the characteristics of the production process currently used to produce biodiesel.Refined vegetable oil can be converted into biodiesel by means of a great variety of techniques and technologies, many of which are still not suitable for application on an industrial scale. The solution of greatest interest is homogeneous alkaline transesterification with KOH and methanol. Even when dealing with this type of conversion, it is impossible to establish a universal pattern to describe the conversion or purification stages because there are various possible solutions that make each system different from another. When we look more closely at the state of the art in industrial biodiesel production plants, we also encounter the potential problems introduced by the type and characteristics of the raw materials.Comparing some of the reference solutions that have inspired numerous installations, an optimization analysis was conducted using ASPENPLUS 2006, for the modeling of the process, and modeFRONTIER 4.1 for the optimization procedure. The optimization analysis was carried out using a multi-objective genetic algorithm optimization in order to define the configuration of the main parameters that guarantee the best trade-off between the maximization of the purity of some important compounds and the minimization of energy requirements in the process. The results of this analysis were Pareto frontiers that identify a family of configurations which define the best trade-off between the objectives. Using the Pareto frontiers we then selected the configuration that requires the minimum energy consumption. Among these optimal configurations there is one which guarantees the lowest specific energy consumption while all the optimal configurations obtained respected the requirements of EN 14214, in terms of biodiesel quality.

Modelling investigation on the thermal conductivity of pure liquid, vapour, and supercritical refrigerants and their mixtures by using Heyen EOS

ABSTRACT This work presents a literature survey of the available data regarding the thermal conductivity of refrigerants. About 31 pure refrigerants that contain 7127 data points are selected for the temperature range of 91.35–580.00 K, a pressure range of (0.000111-500) bar, and thermal conductivity range of (0.007–0.27) W m−1 K−1 containing liquid, vapour, and supercritical phases. Seven binary and three ternary mixtures are also collected both in liquid and vapour phases with an overall of 803 data points. Based on the similarity between the pressure-volume-temperature and Tλ (thermal conductivity) P diagrams, the thermal conductivity model based on Heyen equation of state has been developed for pure refrigerants and their mixtures. The genetic algorithm is used to determine the adjustable parameters of the model. The calculation results prove that this proposed model can reproduce and predict thermal conductivity of refrigerants with good accuracy (overall AAD = 6.85% for pure compounds, AAD = 6.14% for binary mixtures and AAD = 9.32% for ternary mixtures).

Liquid thermal conductivity prediction for alkanes, ketones and silanes

ABSTRACT The values of thermal conductivity at different temperatures for organic and inorganic compounds in the liquid phase is required in the study of several processes, but experimental data are often not available with acceptable reliability or not available at all; since rigorous theoretical or semi-theoretical models of the liquid state are usually of poor practical use for engineering purposes (the errors can be very high and the mathematical difficulties generally lead to excessive simplifications) empirical or semi-empirical methods are used to estimate with reasonable accuracy. In this work a simple equation already proposed by the authors for the estimation of the liquid thermal conductivity of alcohols is generalized in order to allow the calculation of for the compounds belonging to the families of n-alkanes, ketones and silanes. The proposed equation requires for each compound the knowledge of the critical temperature Tc and the molecular weight M and contains three parameters: the ‘golden ratio’ , a factor h characteristic of the investigated organic family and an exponent a depending on the molecular structure. The families of n-alkanes, ketones and silanes were chosen to verify the general reliability of the method when used in large temperature ranges for very different organic families, above all the silanes (compounds containing silicon), whose liquid thermal conductivity is experimentally investigated in very few cases. The comparison between estimated and experimental values was developed taking into account the database due to ‘The DIPPR Information and Data Evaluation Manager’. The general equation appears to be successful: in the reduced temperature range 0.30–0.80, along or near the saturation line, the average absolute deviations between calculated and experimental thermal conductivity data are usually <4% and the maximum absolute ones usually <8%. A comparison is developed between the proposed equation and several correlations appeared in scientific and technical literature. The final goal to be reached is to cover by the proposed estimation method in general the different families of the organic compounds.

New Equation for the Liquid Viscosity of Silanes

This work presents a new formula to calculate the liquid viscosity of the inorganic family of silicon-based molecules. As a first step, the raw viscosity experimental data of silanes were collected...

Artificial neural network modelling of liquid thermal conductivity for alcohols

ABSTRACT This study investigates the applicability of artificial neural networks (ANNs) as an efficient tool for the description of thermal conductivity of liquid alcohols for a broad range of temperatures. The proposed multilayer perceptron has 1 hidden layer with 43 neurons, determined according to the constructive approach. The model developed was trained and validated on the set of data gathered, showing that the accuracy of the ANN model is higher than that of other approaches proposed in the literature. The experimental or experimental and predicted thermal conductivity data of alcohols were taken from the database due to the ‘DIPPR Database’. The ability of the ANN method to reproduce the original data was tested for 26 alcohols in the liquid phase at reduced temperatures ranging from 0.30 to 0.90. The maximum absolute deviations between experimental and calculated thermal conductivity data points are generally less than 0.0110%, while the average absolute deviations are usually less than 0.0016%. This study shows that the model used is a good alternative to estimating thermal conductivity of alcohols with excellent precision.

A modified Kardos equation for the thermal conductivity of refrigerants

This work presents a modification of the Kardos equation specifically oriented to refrigerants. The proposed equation was tested for both liquid and vapor thermal conductivities along saturation of the main refrigerants. In the Kardos equation, the thermal conductivity of liquids is a function of the density of the liquid, heat capacity at constant pressure, speed of sound in the liquid and average distance between the centers of the molecules. In the present version, the liquid molar volume and the distance between the surfaces of adjacent molecules were replaced by two constant parameters widely available for all the fluids: the critical density and radius of gyration. In this way, the resulting equation is much simpler, still being a scaled equation. In the proposed equations, an adimensional factor was regressed to minimize the deviations. The final equations were able to predict the thermal conductivity with AADL=3.6% for liquids and AADV=9.8% for vapors.

Artificial neural network for the second virial coefficient of organic and inorganic compounds: An ANN for B of organic and inorganic compounds

ABSTRACT An artificial neural network (ANN) to estimate the second virial coefficient, valid for organic and inorganic compounds, is presented. First, we statistically analyzed 6,531 experimental points, belonging to 234 fluids, collected from literature. The data were investigated with a factor analysis approach to identify the most significant parameters that influence the second virial coefficient. The factor analysis, combined with physical considerations, allowed to find four (Tr, Tc, Pc, ω) or five (μr) parameters as input variables for the ANN, according to the specific chemical family. The architecture of the proposed multi-layers perceptron (MLP) neural network consists of one input layer with five input variables (Tr, Tc, Pc, ω, μr), one output layer with one neuron (B) and two-hidden-layers with 19 neurons each. We trained, validated and tested several configurations of the neural network to obtain this network topology that minimizes the deviations between experimental and calculated points. Results show that the ANN is able to calculate the second virial coefficient with greater accuracy (RMSE = 29.38 cm3/mol) than that of correlations available in literature. To identify the outliers and applicability domain of the proposed MLP neural network, an outlier diagnosis based on the Leverage approach was performed. This analysis shows that the model is statistically valid.