Multi-objective optimization and 4E (energy, exergy, economy, environmental impact) analysis of a triple cascade refrigeration system.

The post-pandemic energy crisis and ever-increasing environmental degradation necessitate researchers to scrutinize refrigeration systems, major contributors to these issues, for minimal environmental impact and maximum performance. Thus, this study aims to comprehensively examine a triple cascade refrigeration system (TCRS) equipped with hydrocarbon refrigerants (1-butene/Heptane/m-Xylene). This system is specifically designed for ultra-low temperature applications, including vaccine storage, quick-freezing, frozen food preservation, cryogenic processes, and gas liquefaction. The investigation integrates conventional thermodynamic analysis with economic and environmental impact assessments, and finally multi-objective optimization (MOO) to ascertain optimal operating conditions for the system. The effect of (1) evaporator temperature, Tevap (2) condenser temperature, Tcond (3) Lower Temperature Circuit (LTC) condenser temperature, TLTC (4) Mid Temperature Circuit (MTC) condenser temperature, TMTC and (5) Cascade Condenser temperature difference, Δ T on three objective functions (COP, exergy efficiency, and overall plant cost) have been investigated employing a parametric analysis. Subsequently, quadratic equations for these objective functions are generated using the Box-Behnken method, and MOO utilizing the Genetic algorithm has been performed to maximize COP and exergy efficiency while minimizing the overall cost rate. The decision-making techniques TOPSIS and LINMAP are used to retrieve a unique solution from the Pareto Front, and the system performance has been assessed at the optimal point. The optimization result demonstrates that for the 10-kW capacity TCRS, COP, exergy efficiency, and total plant cost are 0.71, 0.51, and 38262.05 $/year respectively, at optimum condition (Tevap = -101.023 °C , Tcond = 36.545 °C , TLTC = - 69.047 °C and TMTC = - 34.651 °C ). Exergy analysis identifies HTC compressor (19.3 %) and throttle valve (15.5 %) as key contributors to total exergy destruction, while economic analysis underscores capital and maintenance costs (72 %) as the primary contributors to the overall cost, with evaporator (43 %) and condenser (20 %) accounting for 63 % of this cost.

Biochar Benefits Green Infrastructure: Global Meta-Analysis and Synthesis.

Urbanization has degraded ecosystem services on a global scale, and cities are vulnerable to long-term stresses and risks exacerbated by climate change. Green infrastructure (GI) has been increasingly implemented in cities to improve ecosystem functions and enhance city resilience, yet GI degradation or failure is common. Biochar has been recently suggested as an ideal substrate additive for a range of GI types due to its favorable properties; however, the generality of biochar benefits the GI ecosystem function, and the underlying mechanisms remain unclear. Here, we present a global meta-analysis and synthesis and demonstrate that biochar additions pervasively benefit a wide range of ecosystem functions on GI. Biochar applications were found to improve substrate water retention capacity by 23% and enhance substrate nutrients by 12-31%, contributing to a 33% increase in plant total biomass. Improved substrate physicochemical properties and plant growth together reduce discharge water volume and improve discharge water quality from GI. In addition, biochar increases microbial biomass on GI by ∼150% due to the presence of biochar pores and enhanced microbial growth conditions, while also reducing CO2 and N2O emissions. Overall results suggest that biochar has great potential to enhance GI ecosystem functions as well as urban sustainability and resilience.

Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh.

Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM2.5 and PM10, therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NOX, SO2, CO, and O3, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM2.5 and PM10 while ANN performed best in case of Chattogram and Sylhet for predicting PM2.5. However, in terms of predicting PM10, M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing "ensemble learning" models by combining several best models to advance application of ML in predicting pollutants' concentration in Bangladesh.