Artificial intelligence methods for modeling gasification of waste biomass: a review.

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

Changes occurring in consumption habits of people during COVID-19 pandemic and the water footprint.

In this study, it has been aimed to determine the difference between water footprint values of individuals with different socio-economical levels, living in various cities, before and during COVID-19 pandemic period. For this purpose, a questionnaire study has been made and data obtained because of questionnaire have been processes in a water footprint calculation module. Data obtained from questionnaires have also been evaluated statistically in SPSS application. According to the findings obtained, while average water footprint before COVID-19 pandemic has been calculated as 4178.42 L/day, average water footprint during COVID-19 pandemic period has been calculated as 4606.18 L/day. It was determined that the percentage of participants whose water footprint increased during the COVID-19 pandemic period at all education levels was higher than other participants. When the water footprint values of the participants with an income level of 7000 TL and above were compared with the water footprint values of other income groups, it was observed that the water footprint values of the participants with an income of 7000 TL and above increased during the COVID-19 pandemic compared to before the COVID-19 pandemic. When the water footprint values of individuals according to age groups are examined, it has been determined that the water footprint values of individuals tend to increase in all age groups (except for the 51-60 age range) during the COVID-19 pandemic compared to before the COVID-19 pandemic. It has been seen that in the monthly clothing expenses and car washing numbers of participants, there was a tendency to decrease and that in their monthly kitchen expenditures there was a tendency to increase. Because of statistical evaluations, it was seen that there was a meaningful correlation between change in water footprint values and weekly shower numbers, weekly laundry washing numbers, and monthly kitchen expenses. Despite the increase in water consumption with many daily activities, it can be said that the average water footprint value did not increase much due to the decrease in clothing expenditures of the participants during the pandemic process, the change in car washing frequencies, and the fact that red meat consumption did not increase in general despite the increase in kitchen expenses.

An evaluation of tannery industry wastewater treatment sludge gasification by artificial neural network modeling.

This paper reports on the calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater. Proximate and ultimate analyses of samples were performed. Thermochemical conversion alters the chemical structure of the waste. Dried air was used as a gasification agent at varying flow rates, which allowed the feedstock to be quickly converted into gas by means of different heterogeneous reactions. A lab-scale updraft fixed-bed steel reactor was used for thermochemical conversion of sludge samples. Artificial neural network (ANN) modeling techniques were used to observe variations in the syngas related to operational conditions. Modeled outputs showed that temporal changes of model predictions were in close accordance with real values. Correlation coefficients (r) showed that the ANN used in this study gave results with high sensitivity.