Artificial Neural Network Modeling of Glass Transition Temperatures for Some Homopolymers with Saturated Carbon Chain Backbone

The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure–property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg’s neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output

Technological and Economic Optimization of Wheat Straw Black Liquor Decolorization by Activated Carbon

Wheat straws are a globally abundant agro-waste that may play a critical role in the global transition from single-use plastics to green materials as an inexpensive and renewable raw material. Vast amounts of wastewater are produced during the technological process of wheat straw-cellulose/hemicellulose conversion. In this context, this work focuses on wastewater decolorization via activated carbon adsorption. A set of carefully planned experiments enabled the identification of a model that described the relationship between the system’s outputs and parameters. While process optimization is frequently connected with identifying process parameters that improve efficiency, this work employed a multi-objective optimization approach from both a technological and economic aspect. Nondominated sorting genetic algorithm versions II and III—NSGA-II and NSGA-III algorithms—were applied. As objectives, maximum efficiency and minimum cost per experiment were followed in different scenarios using pseudoweights and trade-off metrics. When optimizing only the efficiency, the results indicated a 95.54% decolorization yield, costing 0.1228 Euro/experiment, and when considering both the efficiency and cost, different solutions were obtained. The lowest cost was 0.0619, with a 74.42% decolorization. These findings indicate that incorporating an economic perspective into the optimization procedure can improve cost estimation and facilitate managerial decision-making

Obtaining Bricks Using Silicon-Based Materials: Experiments, Modeling and Optimization with Artificial Intelligence Tools

In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process was analyzed with artificial intelligence tools, with the goal of minimizing the exhaust emissions of CO and CH4. Optimization algorithms inspired by human and virus behaviors were applied in this approach, which were associated with neural network models. A series of feed-forward neural networks have been developed, with 6 inputs corresponding to the working conditions, one or two intermediate layers and one output (CO or CH4, respectively). The results for ten biologically inspired algorithms and a search grid method were compared successfully within a single objective optimization procedure. It was established that by introducing 1.9% sunflower seed husks and 0.8% sawdust in the brick manufacturing mix, a minimum quantity of CH4 emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

This study highlights the benefits of optimizing the decolorization of bromocresol green (a colorant/pH indicator widely used in the industry, whose degradation produces toxic byproducts) by adsorption on active carbon. A set of experiments were planned and performed based on the design of experiments methodology for the following parameters: the colorant concentration (0.009-0.045 g/L), the amount of adsorbent (0.5-3 g/L), and the contact time (60-240 min). Modeling and optimization strategies were employed to determine the working conditions leading to efficiency maximization. Using the response surface methodology, the optimum values of the primary process parameters were established. In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. Different solutions were obtained with the considered strategies, and the maximum efficiency obtained was >99%. The study emphasizes that adsorption on active carbon is an effective method for bromocresol green decolorization in wastewater that can be further improved using advanced optimization methods

Optimization of Alkaline Extraction of Xylan-Based Hemicelluloses from Wheat Straws: Effects of Microwave, Ultrasound, and Freeze–Thaw Cycles

The alkaline extraction of hemicelluloses from a mixture of three varieties of wheat straw (containing 40.1% cellulose, 20.23% xylan, and 26.2% hemicellulose) was analyzed considering the following complementary pre-treatments: freeze–thaw cycles, microwaves, and ultrasounds. The two cycles freeze–thaw approach was selected based on simplicity and energy savings for further analysis and optimization. Experiments planned with Design Expert were performed. The regression model determined through the response surface methodology based on the severity factor (defined as a function of time and temperature) and alkali concentration as variables was then used to optimize the process in a multi-objective case considering the possibility of further use for pulping. To show the properties and chemical structure of the separated hemicelluloses, several analytical methods were used: high-performance chromatography (HPLC), Fourier-transformed infrared spectroscopy (FTIR), proton nuclear magnetic resonance spectroscopy (1H-NMR), thermogravimetry and derivative thermogravimetry analysis (TG, DTG), and scanning electron microscopy (SEM). The verified experimental optimization result indicated the possibility of obtaining hemicelluloses material containing 3.40% glucan, 85.51% xylan, and 7.89% arabinan. The association of hot alkaline extraction with two freeze–thaw cycles allows the partial preservation of the hemicellulose polymeric structure

Rhizobacteria and plant symbiosis in heavy metal uptake and its implications for soil bioremediation

Certain species of plants can benefit from synergistic effects with plant growth-promoting rhizobacteria (PGPR) that improve plant growth and metal accumulation, mitigating toxic effects on plants and increasing their tolerance to heavy metals. The application of PGPR as biofertilizers and atmospheric nitrogen fixators contributes considerably to the intensification of the phytoremediation process. In this paper, we have built a system consisting of rhizospheric . Azotobacter microbial populations and . Lepidium sativum plants, growing in solutions containing heavy metals in various concentrations. We examined the ability of the organisms to grow in symbiosis so as to stimulate the plant growth and enhance its tolerance to Cr(VI) and Cd(II), to ultimately provide a reliable phytoremediation system. The study was developed at the laboratory level and, at this stage, does not assess the inherent interactions under real conditions occurring in contaminated fields with autochthonous microflora and under different pedoclimatic conditions and environmental stresses. . Azotobacter sp. bacteria could indeed stimulate the average germination efficiency of . Lepidium sativum by almost 7%, average root length by 22%, average stem length by 34% and dry biomass by 53%. The growth of . L. sativum has been affected to a greater extent in Cd(II) solutions due its higher toxicity compared to that of Cr(VI). The reduced tolerance index (TI, %) indicated that plant growth in symbiosis with PGPR was however affected by heavy metal toxicity, while the tolerance of the plant to heavy metals was enhanced in the bacteria-plant system.A methodology based on artificial neural networks (ANNs) and differential evolution (DE), specifically a neuro-evolutionary approach, was applied to model germination rates, dry biomass and root/stem length and proving the robustness of the experimental data. The errors associated with all four variables are small and the correlation coefficients higher than 0.98, which indicate that the selected models can efficiently predict the experimental data.</p