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Dried, bitter orange leaves are widely used because of their nutritious and medicinal applications. As a result, many technologies have been used to accomplish its drying process. However, drying needs a long time and high energy demand, especially in humid climates. In this paper, bitter orange leaf drying was carried out using thermal and photovoltaic solar energy (integrated system, IS), eliminating the high humidity inside of the drying chamber to improve this process. A regular solar dryer (RD) was also used to compare the kinetics, mathematical modeling, and colorimetry study (as a quality parameter), evaluating both systems’ performances. The drying leaves’ weights were stabilized after 330 min in the RD and after 240 min in the IS, with a maximum drying rate of 0.021 kg water/kg dry matter∙min, reaching a relative humidity of 7.9%. The Page and Modified Page models were the best fitting to experimental results with an Ra2 value of 0.9980. In addition, the colorimetric study showed a better-preserved color using the IS, with an ∆E of 9.12, while in the RD, the ∆E was 20.66. Thus, this system implementation can reduce agroindustry costs by reducing time and energy with a better-quality and sustainable product, avoiding 53.2 kg CO2 emissions to the environment.

An assessment of the thermal refurbishment of an outpatient medical center in a tropical location, such as the City of San Francisco de Campeche, was presented with the aim to diminish its energy consumption. A year-long energy audit of the facility was conducted to formulate and validate a numerical simulation model while scrutinizing enhancement strategies. The examined improvement alternatives encompass passive adjustments to the roof (utilizing insulating materials, applying reflective coatings, and installing a green roof), modifications to active systems incorporating inverter technology, and alterations to the walls via reflective paint. The outcomes of the simulated enhancement scenarios were assessed utilizing energy, environmental, and economic metrics: key performance index (KPI), equivalent CO2 emission index (CEI), and net savings (NS). These results were subsequently juxtaposed against TOPSIS decision-making algorithms to ascertain the alternative that optimally balances the three options. It was identified that using reflective paint on the roof provides the best energy benefits and contributes to mitigating emissions from electricity use. Furthermore, combining this passive technology with the integration of inverter air conditioning systems offers the best economic return at the end of 15 years. For its part, the TOPSIS method indicated that by prioritizing the financial aspect, the reflective coating on the roof combined with inverter air conditioning is enough. However, adding a wall with insulating paint brings environmental and energy benefits. The results of this work serve as a starting point for the analysis of other post-occupied buildings in the region and others under tropical climatic conditions.

This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under aworst-case scenario (ammonia flow rate < 1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate > 2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.

This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under a worst-case scenario (ammonia flow rate<1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate>2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.

This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under a worst-case scenario (ammonia flow rate<1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate>2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.