BPSO-MLR and ANFIS based modeling of lower flammability limit

Towards a circular economy: A comprehensive study of higher heat values and emission potential of various municipal solid wastes

Maximizing resource recovery from waste streams (e.g., energy) is a critical challenge for municipalities. Utilizing the ultimate analysis and high heat value (HHV), we investigated the energy recovery and emission characteristics for 252 solid wastes of a diverse range of geographical origins classifications (e.g., 30 paper, 12 textile, 12 rubber and leather, 29 MSW mixture, 34 plastic, 61 wood, 20 sewage sludge and 53 other wastes) under the thermal waste-to-energy operation. Given the significance of wastes' HHV data, we proposed a rapid and cost-effective methodology for filling the gaps in the experimental data by prediction of the missing or uncertain wastes' HHV. We further employed wastes' nitrogen and sulphur contents to assess their atmospheric emissions. The results from this analysis show the highest energy content belonged to plastic waste, but higher levels of air pollution (mainly due to nitrogen and sulfur) could be emitted during thermal energy recovery of sewage sludge, rubber, and textile wastes. Also, we demonstrated more significant potential for recovering energy from plastic, wood, and paper wastes, while emitting less nitrogen and sulphur compounds to the atmosphere. Finally, our presented HHV models outperform concerning generalizability, validity, and accuracy when comparing the obtained results to those of previously published models. The results from this present study are particularly advantageous in designing sustainable thermal waste-to-energy systems to facilitate cities' transition into a circular economy.

Highly accurate prediction of flammability limits of chemical compounds using novel integrated hybrid models

Two novel and highly accurate hybrid models were developed for the prediction of the flammability limits (lower flammability limit (LFL) and upper flammability limit (UFL)) of pure compounds using a quantitative structure-property relationship approach. The two models were developed using a dataset obtained from the DIPPR Project 801 database, which comprises 1057 and 515 literature data for the LFL and UFL, respectively. Multiple linear regression (MLR), logarithmic, and polynomial models were used to develop the models according to an algorithm and code written using the MATLAB software. The results indicated that the proposed models were capable of predicting LFL and UFL values with accuracies that were among the best (i.e. most optimised) reported in the literature (LFL: R2 = 99.72%, with an average absolute relative deviation (AARD) of 0.8%; UFL: R2 = 99.64%, with an AARD of 1.41%). These hybrid models are unique in that they were developed using a modified mathematical technique combined three conventional methods. These models afford good practicability and can be used as cost-effective alternatives to experimental measurements of LFL and UFL values for a wide range of pure compounds.

Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure-A Property Relationship Approach

The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure-property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.

Group-contribution based property estimation and uncertainty analysis for flammability-related properties

This study presents new group contribution (GC) models for the prediction of Lower and Upper Flammability Limits (LFL and UFL), Flash Point (FP) and Auto Ignition Temperature (AIT) of organic chemicals applying the Marrero/Gani (MG) method. Advanced methods for parameter estimation using robust regression and outlier treatment have been applied to achieve high accuracy. Furthermore, linear error propagation based on covariance matrix of estimated parameters was performed. Therefore, every estimated property value of the flammability-related properties is reported together with its corresponding 95%-confidence interval of the prediction. Compared to existing models the developed ones have a higher accuracy, are simple to apply and provide uncertainty information on the calculated prediction. The average relative error and correlation coefficient are 11.5% and 0.99 for LFL, 15.9% and 0.91 for UFL, 2.0% and 0.99 for FP as well as 6.4% and 0.76 for AIT. Moreover, the temperature-dependence of LFL property was studied. A compound specific proportionality constant (K(LFL)) between LFL and temperature is introduced and an MG GC model to estimate K(LFL) is developed. Overall the ability to predict flammability-related properties including the corresponding uncertainty of the prediction can provide important information for a qualitative and quantitative safety-related risk assessment studies.

Estimation of lower flammability limits of C-H compounds in air at atmospheric pressure, evaluation of temperature dependence and diluent effect

Estimation of the lower flammability limits of C-H compounds at 25 °C and 1 atm; at moderate temperatures and in presence of diluent was the objective of this study. A set of 120 C-H compounds was divided into a correlation set and a prediction set of 60 compounds each. The absolute average relative error for the total set was 7.89%; for the correlation set, it was 6.09%; and for the prediction set it was 9.68%. However, it was shown that by considering different sources of experimental data the values were reduced to 6.5% for the prediction set and to 6.29% for the total set. The method showed consistency with Le Chatelier's law for binary mixtures of C-H compounds. When tested for a temperature range from 5 °C to 100 °C, the absolute average relative errors were 2.41% for methane; 4.78% for propane; 0.29% for iso-butane and 3.86% for propylene. When nitrogen was added, the absolute average relative errors were 2.48% for methane; 5.13% for propane; 0.11% for iso-butane and 0.15% for propylene. When carbon dioxide was added, the absolute relative errors were 1.80% for methane; 5.38% for propane; 0.86% for iso-butane and 1.06% for propylene.

A simple modelling approach for prediction of standard state real gas entropy of pure materials

The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r(2)) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r(2) to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.