Minimum Ignition Energy (MIE) prediction models for ignition sensitive fuels using machine learning methods

Synthesis of fluorescent chalcones, photophysical properties, quantitative structure-activity relationship and their biological application

The development of fluorescent pigments is an area of interest in several research fields due to their high sensitivity. In the current study-eight known and three new N,N-dimethylamino-chalcones (12a-k) were synthesized with good yields using the Claisen-Schmidt reaction. For each molecular system, the photophysical properties, including the maximum absorption wavelength (λ_Absorption), molar absorption coefficient (ε), maximum excitation wavelength (λ_Excitation), maximum emission wavelength (λ_Emission), Stokes Shift (Δλ), fluorescence quantum yield (Φ_fl), fluorescence lifetime (τ_fl), radiative and non-radiative rate constants (k_R and k_NR, respectively) were evaluated. Variations in each of these properties were analyzed depending on the substituents present on each compound. To relate the chemical structures of the synthesized compounds to their photophysical properties, Hansch analysis (2D-QSPR) was applied. As a result of Hansch analysis, we found different photophysical properties related to molecular orbitals and the energy of their derivatives (Highest Occupied Molecular Orbital-HOMO, Lowest Unoccupied Molecular Orbital-LUMO, Difference between LUMO-HOMO-ΔLH, Chemical potential-µ, Hardness-η, Softness-S, and electrophilic global index-ω) as well as to the atomic charges on atoms C₅, C_α, C_β, and CO. The application of this type of analysis has made it possible to understand and subsequently design new molecules with defined photophysical properties. Finally, the compounds were use as fluorescent pigment to get living cell imaging on breast cancer cells, obtaining the compound 12a as promissory alternative.

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with R ² > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.