Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) - Volume 17, Issue 3, 2024

Acoustic parameters can help us understand how temperature and concentration affect the behaviour of potassium ferrocyanide and potassium chromate electrolytes in the aqueous solvent Dimethylformamide.

The solution's density (ρ), viscosity (η), and ultrasonic speed (u) were measured at various concentrations and temperatures (ranging from 293 K to 313 K) using a pycnometer, an Ostwald viscometer, and an ultrasonic interferometer at frequencies of 1MHz, respectively. Based on these measurements, other acoustic parameters were calculated, such as free length (Lr), internal pressure (πi), adiabatic compressibility (β), acoustic impedance (Z), relaxation time (τ), and Gibbs free energy (ΔG).

These acoustic and thermodynamic parameters were used to explore various interactions, molecular motion, and interaction modes, as well as their effects, which were influenced by the size of the pure component and the mixtures. The analysis showed that changes in temperature and concentration led to specific parameter differences, which affected the interactions between the solute and solvent.

This study demonstrated that increasing the concentration of the mixture increased the density, viscosity, and ultrasonic velocity due to the interaction between the solute and solvent, indicating molecular interaction in the mixture.

This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency.

A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance.

Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes.

The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes.

The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood.

The possibility of regulating the curing rate and the complex of adhesive, deformation-strength and dynamic mechanical properties of polymers based on bisphenol A dithioester (thiirane) using a mixture of amine hardeners of various chemical nature is investigated.

Diethylenetriamine, diethylenetriaminomethylphenol and aminopolyamide were investigated as hardeners. The ratio of the components of the mixed hardener is selected, which provides the best combination of strength properties.

It was found that the rate of adhesion and cohesive strength at the initial stage (during the first hour) of curing compositions containing a mixture hardener significantly exceeds compositions cured by individual components of the mixture.

The results of measuring the dynamic mechanical characteristics of the studied polymers indicate that the dynamic modulus of elasticity, measured at temperatures below and above the transition from a glassy state to a high elastic one, for a sample containing a mixed hardener has an intermediate value between the values characteristic of samples containing individual components of a mixed hardener.

Most NN (neural network) research only conducted qualitative analysis, analyzing its accuracy, with certain limitations, without studying its NN model, error convergence process, and pressure ratio. There is relatively limited research on the application of NN optimized by GA (genetic algorithm) to oil and gas pipelines; Moreover, the residual strength evaluation of GA-BP NN (genetic algorithm backpropagation neural network) has the advantages of high global search ability, efficiency not limited by constant differences, and the use of probability search instead of path search, which has a wide application prospect.

Using MATLAB software, establish GA-BP NN models under five residual strength evaluation criteria and introduce the relative error of the parameters and the pressure ratio to comprehensively analyze the accuracy and applicability of GA-BP NN.

Firstly, using MATLAB software, a GA-BP NN model was established based on five residual strength evaluation criteria: ASME B31G Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, by changing five factors that affect the residual strength of oil and gas pipelines: diameter, wall thickness, yield strength, corrosion length, and corrosion depth; Second, the trained GA-BP NN model is used to predict the residual strength of the same set of evaluation criteria test data and compared with the calculation results of five residual strength evaluation criteria. The relative error of the parameters and pressure ratio are introduced to comprehensively analyze the accuracy and applicability of the GA-BP NN.

The error convergence time of the BP NN is longer, and the optimized GA-BP NN has a shorter convergence time. By comparing the convergence training times of different models, it can be obtained that for the five sets of residual strength evaluation criteria of ASME B31G Modified, BS7910, PCORRC, DNV RP F101, and SHELL92, the optimized GA-BP NN model significantly reduces convergence training times, significantly improves convergence speed, and further evolves the system performance. From the relative error and local magnification, it can be seen that for the ASME B31G Modified evaluation criteria, the maximum relative error of the BP NN model is 1.4008%, and the maximum relative error of the GA-BP NN model is 0.7304%. For the evaluation criterion BS7910, the maximum relative error of the BP NN model is 0.7239%, and the maximum relative error of the GA-BP NN model is 0.5242%; for the evaluation criteria of DNV RP F101, the maximum relative error of the BP NN model is 1.1260%, and the maximum relative error of the GA-BP NN model is 0.4810%; for the PCORRC evaluation criteria, the maximum relative, error and the maximum relative error of the GA-BP NN model is 0.8004%; for the SHELL92 evaluation criterion, the maximum relative error of the BP NN model is 1.2292%, and the maximum relative error of the GA-BP NN model is 0.8346%. The results of the GA-BP NN prediction are closer to the results of the calculation of the five residual strength evaluation criteria, and the prediction effect is better, which can more accurately predict the residual strength of the oil and gas pipelines. Based on the pressure ratio, the average pressure ratio A of the BP NN model under the five residual strength criteria is 1.0004, and the average pressure ratio A of the GA-BP NN model is 0.9998. The results predicted by the GA-BP NN model are more accurate.

These findings have crucial implications for the forecast of the residual strength of corrosive oil and gas pipelines.