Prediction of Minimum Ignition Energy from Molecular Structure Using Quantitative Structure–Property Relationship (QSPR) Models

Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor

Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.

An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery

Accurate characterization of molecular representations plays an important role in the property prediction based on deep learning (DL) for drug discovery. However, most previous researches considered only one type of molecular representations, resulting in that it difficult to capture the full molecular feature information. In this study, a novel DL framework called multi-modal molecular representation learning fusion network (MMRLFN) is developed, which could simultaneously learn and integrate drug molecular features from molecular graphs and SMILES sequences. The developed MMRLFN method is composed of three complementary deep neural networks to learn various features from different molecular representations, such as molecular topology, local chemical background information, and substructures at varying scales. Eight public datasets involving various molecular properties used in drug discovery were employed to train and evaluate the developed MMRLFN. The obtained models showed better performances than the existing models based on mono-modal molecular representations. Additionally, a thorough analysis of the noise resistance and interpretability of the MMRLFN has been carried out. The generalization ability and effectiveness of the MMRLFN has been verified by case studies as well. Overall, the MMRLFN can accurately predict molecular properties and provide potentially valuable information from large datasets, thereby maximizing the possibility of successful drug discovery.

Combined DFT and Machine Learning Study of the Dissociation and Migration of H in Pyrrole Derivatives

Systematic DFT calculations of model coal-pyrrole derivatives substituted by different functional groups are carried out. The N-H bond dissociation energies (N-H BDEs) and H-transfer activation energies (H-TAEs) of pyrrole derivatives are fully evaluated to elucidate the effect of the type of substituents and their position on the molecular reactivity. The results indicate that compounds substituted with electron-donating groups (EDGs) are more prone to pyrolysis while those substituted with electron-withdrawing groups (EWGs) are difficult to pyrolyze. Furthermore, quantitative structure-property relationship (QSPR) models for N-H BDEs and H-TAEs about pyrrole derivatives are built with multiple linear regression (MLR) and support vector machine (SVM). The final results show that the SVM-QSPR model has better fitness, prediction, and robustness, while the MLR-QSPR model can express the physical meaning better. The effects of functional groups on pyrolysis are clarified by the models presented in this paper, which will support the optimization of ultra-low NOx combustion.

SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model’s significance. ANN modelling with 13 hidden layers gives the highest R2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning

The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO₂ adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (g_CO2/g_sorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure-property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

Liquid flammability ratings predicted by machine learning considering aerosolization

Liquid flammability is classified based on flash point as in NFPA 704, GHS, and OSHA. However, flash points become insignificant when the liquid is in aerosol form, which is evident from numerous incidents revealing that a liquid can be ignited below its flash point when an aerosol. In this study, two machine learning (ML) methods are utilized to propose liquid flammability ratings while considering aerosolization. 823 compounds from the Design Institute for Physical Properties 801 database are used in this study. The first method rates the liquid flammable hazards and probability of aerosolization separately and then uses the proposed safety index to combine the contribution of flammable hazards and aerosolization. The second method uses Principal Component Analysis (PCA) to create two principal components, then clusters the liquids based on these two principal components. The PCA method advange is the weight of each property is automatically considered. A traditional risk assessment utilizes a risk matrix, this study uses two ML clustering algorithms are applied, K-means Clustering (KC) and Hierarchical Clustering (HC). Based on expert judgment, the HC algorithm gives a more reasonable rating of the probability of aerosolization, while the KC algorithm has a more reasonable rating on liquid flammability clustering.