During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( and ). This kind of promising research can contribute significantly to water management.

Pyrolysis of waste low-density polyethylene (LDPE) is considered to be a highly efficient, promising treatment method. This work aims to investigate the kinetics of LDPE pyrolysis using three model-free methods (Friedman, Flynn-Wall-Qzawa (FWO), and Kissinger-Akahira-Sunose (KAS)), two model-fitting methods (Arrhenius and Coats-Redfern), as well as to develop, for the first time, a highly efficient artificial neural network (ANN) model to predict the kinetic parameters of LDPE pyrolysis. Thermogravimetric (TG) and derivative thermogravimetric (DTG) thermograms at 5, 10, 20 and 40 K min⁻¹ showed only a single pyrolysis zone, implying a single reaction. The values of the kinetic parameters (E and A) of LDPE pyrolysis have been calculated at different conversions by three model-free methods and the average values of the obtained activation energies are in good agreement and ranging between 193 and 195 kJ mol⁻¹. In addition, these kinetic parameters at different heating rates have been calculated using Arrhenius and Coats-Redfern methods. Moreover, a feed-forward ANN with backpropagation model, with 10 neurons in two hidden layers and logsig-logsig transfer functions, has been employed to predict the thermogravimetric analysis (TGA) kinetic data. Results showed good agreement between the ANN-predicted and experimental data (R > 0.9999). Then, the selected network topology was tested for extra new input data with a highly efficient performance.

Plastic wastes have become one of the biggest global environmental issues and thus recycling such massive quantities is targeted. Low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP), and polystyrene (PS) are considered among the main types of plastic wastes. Since pyrolysis is one of the most promising recycling techniques, this work aims to build knowledge on the co-pyrolysis of mixed polymers using two model-fitting (Criado and Coats–Redfern) methods. Seventeen co-pyrolysis tests using a thermogravimetric analyzer (TGA) at 60 K/min for different mixed compositions of LDPE, HDPE, PP, and PS were conducted. It was observed that the pyrolysis of the pure polymer samples occurs at different temperature ranges in the following order: PS < PP < LDPE < HDPE. However, compared to pure polymer samples, the co-pyrolysis of all-polymer mixtures was delayed. In addition, the synergistic effect on the co-pyrolysis of polymer blends was reported. The Master plot of the Criado model was used to determine the most suitable reaction mechanism. Then, the Coats–Redfern model was used to efficiently obtain the kinetic parameters (R² ≥ 97.83%) and the obtained values of the activation energy of different polymer blends were ranging from 104 to 260 kJ/mol. Furthermore, the most controlling reaction mechanisms were in the following orders: First order reaction (F1), Contracting sphere (R3), and then Contracting cylinder (R2).

Flow assurance is critical and difficult for deepwater pipeline since the seawater temperature, surrounding pipeline, is usually much colder than the surface air temperature. In the deepwater, the fluid heat can be quickly lost to the water if there is no thermal insulation layer surrounding the pipe wall. When the inner wall temperature falls below the crude oil cloud point temperature, wax deposition occurs. Such problem starts when paraffin components in crude oil precipitate and deposit on the cold pipeline wall. Whereas wax precipitation during oil flow results in wax deposition and flow restriction, wax precipitation during a production shutdown results in problems when attempting to restart the flow. In this review, wax deposition problem in flow and during the shut-in conditions are discussed. Also, proposed wax deposition physics and mechanisms are outlined. Furthermore, different wax deposition mitigation techniques are explained. These techniques include: Cold Flow, Chemical Additives and Different Pipes and Coatings Techniques. In addition, different wax deposition removal techniques are reviewed. These techniques include: Fused Chemical Reaction, Mechanical Techniques, Heat Application, Chemical Additives, Magnetic Fluid Conditioning (MFC) technology and Use of Microbial Products.

The utilization of a high-volume of treated palm oil fuel ash (T-POFA) as a partial cement substitution is one of the solutions presented to reduce carbon dioxide emissions (CO₂) and improve concrete sustainability. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is adapted as an artificial neural network (ANN) modeling tool to predict the compressive strength of self-compacting concrete (SCC) containing T-POFA. The ANFIS model has been developed and validated using concrete mixtures incorporating 0%, 10 wt%, 20 wt%, 30 wt%, 50 wt%, 60 wt%, and wt 70% T-POFA as a replacement of ordinary Portland cement (OPC) at a constant water/binder (W/B) ratio of 0.35. The experimental data were divided into 70% training data and 30% testing data. The experimental results of self-compacting concrete (SCC) containing T-POFA ensured comparable or higher compressive strengths, especially at later ages, when compared to the control SCC. However, the prediction results of the compressive strength of SCC samples using the ANFIS model are very close to the experimental values. The developed ANFIS model showed a highly-efficient performance to predict the SCC compressive strength. In addition, the obtained accurate predicted results using the developed ANN model will significantly affect the current experimental protocols, especially for costly and unsafe experiments.