This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents—hexane, chloroform, and acetone—with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10−3 to 3.70 × 10−7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.

Heavy metals are toxic substances that pose a real danger to humans and organisms, even at low concentration. Therefore, there is an urgent need to remove heavy metals. Herein, the nanocellulose (NC) was synthesized by the hydrolysis of cellulose using sulfuric acid, and then functionalized using polypyrrole (ppy) through a polymerization reaction to produce polypyrrole/nanocellulose (ppy/NC) nanocomposite. The synthesized nanocomposite was characterized using familiar techniques including XRD, FT-IR, SEM, TEM, and TGA. The obtained results showed a well-constructed nanocomposite with excellent thermal stability in the nano-sized scale. The adsorption experiments showed that the ppy/NC nanocomposite was able to adsorb hexavalent chromium (Cr(VI)). The optimum pH for the removal of the heavy metal was pH 2. The interfering ions showed minor effect on the adsorption of Cr(VI) resulted from the competition between ions for the adsorption sites. The adsorption kinetics were studied using pseudo 1st order and pseudo 2nd order models indicating that the pseudo second order model showed the best fit to the experimental data, signifying that the adsorption process is controlled by the chemisorption mechanism. Additionally, the nanocomposite showed a maximum adsorption capacity of 560 mg/g according to Langmuir isotherm. The study of the removal mechanism showed that Cr(VI) ions were removed via the reduction of high toxic Cr(VI) to lower toxic Cr(III) and the electrostatic attraction between protonated ppy and Cr(VI). Interestingly, the ppy/NC nanocomposite was reused for Cr(VI) uptake up to six cycles showing excellent regeneration results. Subsequently, Cr(VI) ions can be effectively removed from aqueous solution using the synthesized nanocomposite as reusable and cost-effective adsorbent.

The unique biological and physicochemical characteristics of biogenic (green-synthesized) nanomaterials (NMs) have attracted significant interest in different fields, with applications in the agrochemical, food, medication delivery, cosmetics, cellular imaging, and biomedical industries. To synthesize biogenic nanomaterials, green synthesis techniques use microorganisms, plant extracts, or proteins as bio-capping and bio-reducing agents and their role as bio-nanofactories for material synthesis at the nanoscale size. Green chemistry is environmentally benign, biocompatible, nontoxic, and economically effective. By taking into account the findings from recent investigations, we shed light on the most recent developments in the green synthesis of nanomaterials using different types of microbes and plants. Additionally, we cover different applications of green-synthesized nanomaterials in the food and textile industries, water treatment, and biomedical applications. Furthermore, we discuss the future perspectives of the green synthesis of nanomaterials to advance their production and applications.

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Vitamin B12 has essential roles in DNA synthesis, red blood cell development, and neurologic functions. Vitamin B12 deficiency is relatively common, particularly in people aged over 60 years. Among hematological disturbances, microangiopathic hemolytic anemia with thrombocytopenia or so-called pseudo-thrombotic microangiopathy (pseudo-TMA) is a particularly rare but significant clinical complication in patients with vitamin B12 deficiency. We herein describe a case of an elderly patient with pseudo-TMA whose lack of vitamin B12 was misdiagnosed as thrombotic thrombocytopenic purpura (TTP). The patient was admitted as a case of pancytopenia with a hemolytic picture. The initial impression was TTP versus acute promyelocytic leukemia M3. After examination of laboratory tests and bone marrow examination, we deduced that the patient had a B12 deficiency. The condition of the patient improved with B12 replacement. This report should remind physicians to widen their differential diagnoses when patients present with microangiopathic hemolysis or in patients who are not responsive to standard treatments for TTP.

This paper proposes a multi-objective evolutionary algorithm for optimizing model base predictive control (MBPC) tuning parameters applied to the boiling process. The multi-objective evolutionary algorithms are able to incorporate many objective functions that can simultaneously meet robust stability and performance that can satisfy control design objective functions. These promising techniques are successfully implemented to stabilise MBPC at the implications of different levels of model uncertainties.The Pareto optimum technique is able to overcome the problem of trapping the standard genetic algorithms (SGAs) in the local optimum when using the LQ as the objective functions at the price of high model uncertainty. Introducing robust stability and performance objective functions has successfully improved the search procedure for MBPC tuning variables at high model uncertainty.

Hitherto, a considerable amount of research has been carried out to investigate the equilibrium condition of adsorption process; nevertheless, there is no comprehensive study to evaluate the surface adsorption properties of MOFs. Therefore, the adsorption mechanism and equilibrium capacity of MOFs have not been fully understood. Furthermore, the mass transfer mechanism is still unknown and so it is not possible to predict the adsorption process using MOFs. In this work, a new metal–organic framework (MOF) named UIO-66–MnFe2O4 was synthesized as an adsorbent for oily wastewater treatment. In this way the effects of temperature, amount of adsorbent, adsorption time, pH, and pollutant initial concentration were studied in the treatment of oily wastewater using the UIO-66-MnFe2O4 MOF through the adsorption process. Furthermore, to examine the process of surface adsorption, different adsorption kinetic models (pseudo-first-order, pseudo-second-order, and Elovich) have been performed for the removal of oily pollutants on MOF adsorbents and the surface adsorption mechanism has been discussed carefully. Moreover, to investigate the mass transfer mechanism of oily pollutants in the surface adsorption process, different mass transfer models (Weber and Morris, liquid film diffusion, and Bangham and Burt) have been investigated on porous adsorbents, and finally the mass transfer mechanism of the adsorption process has been proposed.

In this work, used lube oil was treated using natural acid-free clay. Clay was added at different amounts (5, 10, and 20 g) to 100 mL of waste engine oil at various temperatures (250, 350, 400, and 450 °C) and mixed at a speed of 800 rpm for 30 min. After settling and separation, the treated oil was diluted with kerosene before being examined using a Ultraviolet–visible (UV) spectrophotometer. In order to achieve cost-effective recycling, this process is modeled using the response surface method (RSM). Five regression models (linear, quadratic, Two Factor Interactions (2FI), cubic, and reduced-order quadratic model) were developed, then tested, and examined by calculating the statistical performance indicators (R2, R2adj, Akaike’s Information Criterion corrected (AICc), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE)). The results obtained reveal that the modified quadratic model outperforms the rest of the models in terms of the low value of RMSE, the lowest AICc, lowest BIC, and the highest R2 and R2adj. The developed modified quadratic model is optimized successfully to predict optimum operation conditions. Results show that optimum operation conditions are at the minimum area under the curve for UV absorption at 223.358; this can be achieved with a process temperature of 266.246 °C and clay quantity of 5.331 g. This model agreed with experimental data regardless of the effectiveness of red clay in the therapy of lube oil.