Influences of straw alkaline pretreatment on biogas production and digestate characteristics: artificial neural network and multivariate statistical techniques.

Anaerobic digestion is one of the best options for producing valuable end products (biogas and biofertilizer). The aim of this study was to investigate the influences of thermoalkaline pretreatment of wheat straw on biogas production and digestate characteristics from codigestion with waste-activated sludge. Different alkaline conditions (NaOH, KOH and Na2CO3) and pretreatment durations (1, 3 and 5 h) were used for straw pretreatment. Batch anaerobic codigestion of sludge and pretreated straw was conducted under different pretreatment conditions. A feedforward neural network (FFNN) model, logistic model and statistical analysis were applied to the experimental data to predict biogas and investigate the significance and relationships among the variables. NaOH pretreatment for 5 h showed the best treatment conditions: biogas yield was 6.59 times higher than that without treatment. Moreover, the proportions of total solids, total volatile solids, chemical oxygen demand and microbial count removed reached 63.52%, 74.60%, 78.15% and 82.22%, respectively. The methane content was 67.50%, indicating that the biogas had a high quality. The thermoalkaline pretreatment significantly affected biogas production and digestate characteristics, allowing it to be used as a biofertilizer. Experimental data were successfully modelled for predicting biogas production using the applied models. The R2 values reached 0.985 and 0.999 for the logistic and FFNN models, respectively.

Innovative solid desiccant dehumidification using distributed microwaves.

Dehumidification is one of the key challenges facing the air conditioning (AC) industry in the treatment of moist air. Over many decades, the dual role of heat exchangers of AC chillers for the sensible and latent cooling of space has hindered the thermal-lift reduction in the refrigeration cycle due to the requirements of water vapor removal at dew-point and heat rejection to the ambient air. These practical constraints of AC chillers have resulted in the leveling of energy efficiency of mechanical vapor compressors (MVC) for many decades. One promising approach to energy efficiency improvement is the decoupling of dehumidification from sensible processes so that innovative but separate processes can be applied. In this paper, an advanced microwave dehumidification method is investigated in the laboratory, where the microwave (2.45 GHz) energy can be irradiated onto the dipole structure of water vapor molecules, desorbing rapidly from the pores of adsorbent. Results show a significant improvement in performance for microwave dehumidification, up to fourfold, as compared to data available in the literature.

A scientometric analysis approach on the plastic sand.

The purpose of this research was to conduct a scientometric evaluation of the literature pertaining to plastic sand in order to evaluate its many aspects. Conventional review studies have several limitations when it comes to their capacity to completely and properly link different sections of the published research. Some of the more complicated features of advanced research are co-occurrence analysis, science mapping and co-citation analysis. During the study, the most inventive authors/researchers renowned for citations, the sources with the largest number of publications, the actively involved domains, and co-occurrences of keywords in the research on plastic sand are investigated. This study is limited to scientometric analysis of the available literature data on plastic sand. The VOSviewer application (version 1.6.18) was used to perform the analysis after bibliometric data for 4512 publications were extracted from the Scopus database and utilised in the extraction process from the year 2021 to June 2022. With the support of a statistical and graphical description of researchers and nations that are contributing, this study will aid researchers in the establishment of collaborative ventures and the exchange of fresh techniques and ideas with one another.

Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming.

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming.

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF.

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.