Process modeling of municipal solid waste compost ash for reactive red 198 dye adsorption from wastewater using data driven approaches

New insights in food security and environmental sustainability through waste food management

Food waste has been identified as one of the major factors that constitute numerous anthropogenic activities, especially in developing countries. There is a growing problem with food waste that affects every part of the waste management system, from collection to disposal; finding long-term solutions necessitates involving all participants in the food supply chain, from farmers and manufacturers to distributors and consumers. In addition to food waste management, maintaining food sustainability and security globally is crucial so that every individual, household, and nation can always get food. "End hunger, achieve food security and enhanced nutrition, and promote sustainable agriculture" are among the main challenges of global sustainable development (SDG) goal 2. Therefore, sustainable food waste management technology is needed. Recent attention has been focused on global food loss and waste. One-third of food produced for human use is wasted every year. Source reduction (i.e., limiting food losses and waste) and contemporary treatment technologies appear to be the most promising strategy for converting food waste into safe, nutritious, value-added feed products and achieving sustainability. Food waste is also employed in industrial processes for the production of biofuels or biopolymers. Biofuels mitigate the detrimental effects of fossil fuels. Identifying crop-producing zones, bioenergy cultivars, and management practices will enhance the natural environment and sustainable biochemical process. Traditional food waste reduction strategies are ineffective in lowering GHG emissions and food waste treatment. The main contribution of this study is an inventory of the theoretical and practical methods of prevention and minimization of food waste and losses. It identifies the trade-offs for food safety, sustainability, and security. Moreover, it investigates the impact of COVID-19 on food waste behavior.

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

Insights into isotherms, kinetics, and thermodynamics of adsorption of acid blue 113 from an aqueous solution of nutraceutical industrial fennel seed spent

Research studies have been carried out to accentuate Fennel Seed Spent, a by-product of the Nutraceutical Industry, as an inexpensive, recyclable and operational biosorbent for bioremediation of Acid Blue 113 (AB113) in simulated water-dye samples and textile industrial effluent (TIE). The physical process of adhesion of AB113 on the surface of the biosorbent depends on various parameters, such as the initial amount of the dye, amount and expanse of the biosorbent particles, pH of the solution and temperature of the medium. The data obtained was analyzed using three two-parameter and five three-parameter adsorption isotherm models to glean the adsorbent affinities and interaction mechanism of the adsorbate molecules and adsorbent surface. The adsorption feature study is conducted employing models of Weber-Morris, pseudo 1st and 2nd order, diffusion film model, Dumwald-Wagner and Avrami model. The study through 2nd order pseudo and Avrami models produced complementary results for the authentication of experimental data. The thermodynamic features, ΔG0, ΔH0, and ΔS0 of the adsorption process are acclaimed to be almost spontaneous, physical in nature and endothermic in their manifestation. Surface characterization was carried out using Scanner Electron Microscopy, and identification and determination of chemical species and molecular structure was performed using Infrared Spectroscopy (IR). Maximum adsorption evaluated using statistical optimization with different combinations of five independent variables to study the individual as well as combined effects by Fractional Factorial Experimental Design (FFED) was 236.18 mg g-1 under optimized conditions; pH of 2, adsorbent dosage of 0.500 g L-1, and an initial dye concentration of 209.47 mg L-1 for an adsorption time of 126.62 min with orbital shaking of 165 rpm at temperature 49.95 °C.

Optimization of dye-contaminated wastewater treatment by fungal Mycelial-light expanded clay aggregate composite

Dye-contaminated wastewaters from the printing batik industry are hazardous if discharged into the environment without any treatment. Finding an optimization and reusability assessment of a new fungal-material composite for dye-contaminated wastewater treatment is important for efficiency. The study purposes to optimize fungal mycelia Trametes hirsuta EDN 082 - light expanded clay aggregate (myco-LECA) composite for real priting batik dye wastewater treatment by using Response Surface Methodology with Central Composite Design (RSM-CCD). The factors included myco-LECA weight (2-6 g), wastewater volume (20-80 mL), and glucose concentration (0-10%) were applied for 144 h of incubation time. The result showed that the optimum condition was achieved at 5.1 g myco-LECA, at 20 mL wastewater, and at 9.1% glucose, respectively. In this condition, the decolorization values with an incubation time of 144 h were 90, 93, and 95%, at wavelengths 570, 620, and 670 nm, respectively. A reusability assessment was conducted for 19 cycles and the result showed that decolorization effectiveness was still above 96%. GCMS analysis showed the degradation of most compounds in the wastewater and the degradation products of the wastewater demonstrated detoxification against Vigna radiata and Artemia salina. The study suggests that myco-LECA composite has a good performance and therefore is a promising method for the treatment of printing batik wastewater.

Removal of hazardous textile dye from simulated wastewater by municipal organic solid waste charcoal using machine learning approaches: Kinetics, isotherm, and thermodynamics

This study focuses on the probable use of municipal organic solid waste charcoal (MOSWC) as an adsorbent for Methyl orange (MO) adsorption. The prepared MOSWC is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), adsorbate concentration (60-140 mg/L), adsorbent dose (1-5 g/L), pH (3-11), and temperature (25-60 °C). The high coefficient value (R2 = 0.96) of the process optimization model suggests that this model was significant, where pH and adsorbent dose expressively stimulus adsorption efficiency including 40.11 mg/g at pH (3), MO concentration (100 mg/L), and MOSWC dose (1 g/L). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected values. The highest monolayer adsorption capacity of MO was 90.909 mg/g. Pseudo-second-order was the well-suited kinetics, where Langmuir isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows MO adsorption is favourable, exothermic, and spontaneous. Finally, this study indicates that MOSWC could be a potential candidate for the adsorption of MO from wastewater.

Removal of organic and inorganic effluents from wastewater by using degradation and adsorption properties of transition metal-doped nickel ferrite

Removal of water pollutants (methylene blue dye and heavy metals) was achieved by zinc/manganese-doped nickel ferrites (Ni_{1 - x}M_xFe₂O₄, where x = 0.00, 0.025, 0.10). Degradation of dye was achieved under natural solar light illumination. Degradation studies of dye were conducted under different parameters such as contact time-80 min, dye's concentration-5 mg/L, pH-7, and dosage of ferrites-15 mg. The adsorption of dye was studied using non-linear kinetics models (pseudo-first-order and pseudo-second-order) and isotherm models (Langmuir and Freundlich). The adsorption of dye followed pseudo-first-order kinetics (R² = 0.99377) than second-order kinetics (R² = 0.98063) and Langmuir isotherm model (R² = 0.96095) than Freundlich model (R² = 0.95962) with maximum adsorption efficiency of 29.62 mg/g. Doping of nickel ferrites caused an increase in the removal percentage of methylene blue dye (80 to 90%) and inorganic effluents (75 to 95% for lead and 47 to 82% for cadmium). In addition to this, band gap energy (2.43 to 3.26 eV) (UV-Vis spectroscopy), pore radius (65.2 to 74.8 A°), and specific surface area (16.45 to 27.95 m²/g) (BET analysis) were also increased. Generally, the results of the study revealed that synthesized nanoparticles can act as potential candidate for the removal of effluents from wastewater under optimum parameters along with recyclability, reusability, and separation under the influence of a magnetic field.

Modeling and optimization of chlorpyrifos and glyphosate biodegradation using RSM and ANN: Elucidating their degradation pathways by GC-MS based metabolomics

Ongoing and extensive use of pesticides negatively impact the environment and human health. Microbe-based remediation bears importance as it is an eco-friendly and cost-effective technique. The present study investigated chlorpyrifos (CHL) and glyphosate (GLY) degrading potential of Bacillus cereus AKAD 3-1, isolated from the soybean rhizosphere. Optimization and validation of different process variables were carried out by response surface methodology (RSM) and artificial neural network (ANN). Critical parameters which affect the degradation process are initial pesticide concentration, pH, and inoculum size. At optimum conditions, the bacterial strain demonstrated 94.52% and 83.58% removal of chlorpyrifos and glyphosate, respectively. Both Central-composite design (CCD-RSM) and ANN approaches proved to perform well in modeling and optimizing the growth conditions. The optimum ANN-GA model resulted in R² ≥ 0.99 for chlorpyrifos and glyphosate, while in the case of RSM, the obtained R² value was 0.96 and 0.95, respectively. Results indicated that the process variables significantly (p < 0.05) impact chlorpyrifos and glyphosate biodegradation. Moreover, the predicted RSM model had a "lack of fit p-value" of "0.8849" and "0.2502" for chlorpyrifos and glyphosate, respectively. GC-MS analysis revealed that the strain first converted chlorpyrifos into 3,5,6-trichloro pyridin-2-ol & O, O-diethyl O-hydrogen phosphorothiate. Later, these intermediate metabolites were broken and completely mineralized into non-toxic by-products. Similarly, glyphosate was first converted into 2-(methylamino) acetic acid and amino-oxyphosphonic acid, which were further mineralized without any toxic by-products. Taken together, the results of this study clarify the biodegradation pathways and highlights the promising potential of B. cereus AKAD 3-1 in the bioremediation of chlorpyrifos and glyphosate-polluted environments.

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.

Adsorption behaviors and mechanisms of Cu2+, Zn2+ and Pb2+ by magnetically modified lignite

The study aims to solve the problems of limited capacity and difficult recovery of lignite to adsort Cu²⁺, Zn²⁺ and Pb²⁺ in acid mine wastewater (AMD). Magnetically modified lignite (MML) was prepared by the chemical co-precipitation method. Static beaker experiments and dynamic continuous column experiments were set up to explore the adsorption properties of Cu²⁺, Zn²⁺ and Pb²⁺ by lignite and MML. Lignite and MML before and after the adsorption of heavy metal ions were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectrometer (FTIR). Meanwhile, the adsorption mechanisms of Cu²⁺, Zn²⁺ and Pb²⁺ by lignite and MML were revealed by combining the adsorption isotherm model and the adsorption kinetics model. The results showed that the pH, adsorbent dosage, temperature, initial concentration of heavy metal ions, and contact time had an influence on the adsorption of Cu²⁺, Zn²⁺ and Pb²⁺ by lignite and MML, and the adsorption processes were more in line with the Langmuir model. The adsorption kinetics experiments showed that the adsorption processes were jointly controlled by multiple adsorption stages. The adsorption of heavy metal ions by lignite obeyed the Quasi first-order kinetic model, while the adsorption of MML was chemisorption that obeyed the Quasi second-order kinetic model. The negative ΔG and positive ΔH of Cu²⁺ and Zn²⁺ indicated the spontaneous and endothermic nature reaction, while the negative ΔH of Pb²⁺ indicated the exothermic nature reaction. The dynamic continuous column experiments showed that the average removal rates of Cu²⁺, Zn²⁺ and Pb²⁺ by lignite were 78.00, 76.97 and 78.65%, respectively, and those of heavy metal ions by MML were 82.83, 81.57 and 83.50%, respectively. Compared with lignite, the adsorption effect of MML was better. As shown by SEM, XRD and FTIR tests, Fe₃O₄ was successfully loaded on the surface of lignite during the magnetic modification, which made the surface morphology of lignite coarser. Lignite and MML removed Cu²⁺, Zn²⁺ and Pb²⁺ from AMD in different forms. In addition, the adsorption process of MML is related to the O–H stretching vibration of carboxylic acid ions and the Fe–O stretching vibration of Fe₃O₄ particles.

Application of Optimization and Modeling for the Enhancement of Composting Processes

Composting is a more environmentally friendly and cost-effective alternative to digesting organic waste and turning it into organic fertilizer. It is a biological process in which polymeric waste materials contained in organic waste are biodegraded by fungi and bacteria. Temperature, pH, moisture content, C/N ratio, particle size, nutrient content and oxygen supply all have an impact on the efficiency of the composting process. To achieve optimal composting efficiency, all of these variables and their interactions must be considered. To this end, statistical optimization techniques and mathematical modeling approaches have been developed over the years. In this paper, an overview of optimization and mathematical modeling approaches in the field of composting processes is presented. The advantages and limitations of optimization and mathematical modeling for improving composting processes are also addressed.

Multivariate optimization of removing of cobalt(II) with an efficient aminated-GMA polypropylene adsorbent by induced-grafted polymerization under simultaneous gamma-ray irradiation

Nowadays, radiation grafting polymer adsorbents have been widely developed due to their advantages, such as low operating cost, high efficiency. In this research, glycidyl methacrylate monomers were grafted on polypropylene polymer fibers by simultaneous irradiation of gamma-ray with a dose of 20 kGy. The grafted polymer was then modified using different amino groups and tested for adsorption of cobalt ions in an aqueous solution. Finally, the modified polymer adsorbent with a high efficiency for cobalt ions adsorption was synthesized and tested. Different modes of cobalt ions adsorption were tested in other adsorption conditions, including adsorption contact time, pH, different amounts of adsorbent mass, and different concentrations of cobalt ions solution. The adsorbent structure was characterized with FT-IR, XRD, TG and SEM techniques and illustrated having an efficient grafting percentage and adsorption capability for cobalt removing by batch experiments. The optimum conditions were obtained by a central composite design: adsorbent mass = 0.07 g, initial concentration = 40 mg/L, time = 182 min, and pH = 4.5 with ethylenediamine as a modified monomer and high amination percentage. Kinetics and equilibrium isotherms observation described that the experimental data followed pseudo-second-order and Langmuir models, respectively. The maximum adsorption capacity from Langmuir isotherm capacity is obtained equal to 68.02 mg/g.

Factors Affecting Synthetic Dye Adsorption; Desorption Studies: A Review of Results from the Last Five Years (2017-2021)

The primary, most obvious parameter indicating water quality is the color of the water. Not only can it be aesthetically disturbing, but it can also be an indicator of contamination. Clean, high-quality water is a valuable, essential asset. Of the available technologies for removing dyes, adsorption is the most used method due to its ease of use, cost-effectiveness, and high efficiency. The adsorption process is influenced by several parameters, which are the basis of all laboratories researching the optimum conditions. The main objective of this review is to provide up-to-date information on the most studied influencing factors. The effects of initial dye concentration, pH, adsorbent dosage, particle size and temperature are illustrated through examples from the last five years (2017-2021) of research. Moreover, general trends are drawn based on these findings. The removal time ranged from 5 min to 36 h (E = 100% was achieved within 5-60 min). In addition, nearly 80% efficiency can be achieved with just 0.05 g of adsorbent. It is important to reduce adsorbent particle size (with Φ decrease E = 8-99%). Among the dyes analyzed in this paper, Methylene Blue, Congo Red, Malachite Green, Crystal Violet were the most frequently studied. Our conclusions are based on previously published literature.