Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized.

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO₂) for particle engineering. SCCO₂ has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO₂ systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10⁻⁸, 2.173 × 10⁻⁹, and 1.372 × 10⁻⁸, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10⁻³ as an output.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO₂, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO₂SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.