No abstract

Various external-recycle configurations of multi-pass flat-plate solar air collectors were studied theoretically to examine the optimal thermal performance under the same working dimension and operation conditions. An absorber plate and insulation sheet were implemented horizontally and vertically, respectively, into an open rectangular conduit to conduct a recycling four-pass operation, which the device lengthens the air flow channel and increases the air mass flow rate within the collector, and thus, a more heat transfer efficiency is obtained. Four recycling types with different external-recycle patterns were introduced and expected to augment the heat transfer rate due to the turbulent convective intensity through four subchannels in the present study. Coupling energy balances into one-dimensional modeling equations were derived by making the energy-flow diagram within a finite element, which the longitudinal temperature distributions for each subchannel were obtained. The theoretical predictions show that the improved four-pass device is accomplished due to the multiple heating pathways over and under the absorber plate, from which the turbulence intensity augmentation results in the heat transfer rate as compared to that in the device without inserting the absorber plate and insulation sheet (say a downward-type single-pass solar air collector). The theoretical results also show that the external-recycle configuration (say Type C in the present study) acts as an optimal collector thermal efficiency and leading to a beneficial design in multi-pass solar air collectors for improving heat-transfer rate and increasing resident time under the same operation conditions. Theoretical predictions show a higher heat-transfer efficiency for the present recycling configurations up to a maximum 115% device enhancement in comparison to that of a single-pass device. Examination of implementing the absorber plate and insulation sheet on the heat-transfer efficiency enhancement as well as the hydraulic dissipated power increment were also delineated, and deliberated the suitable external-recycle configuration with respect to an economic consideration.

Stretch blow molding serves as the primary technique employed in the production of polyethylene terephthalate (PET) bottles. Typically, a stretch blow molding machine consists of various components, including a preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Of particular significance is the temperature control within the heating system, which significantly influences the quality of PET bottles, especially when confronted with environmental temperature changes between morning and evening during certain seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process heavily relies on the personnel’s experience, causing a production challenge for bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent approach for adjusting temperature control parameters. The proposed approach aims to address issues such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. Experimental results demonstrate that the proposed approach achieves automatic adjustment of temperature control parameters during the heating process, effectively mitigating the influence of environmental temperature changes and ensuring stable control of preform surface temperature within ±2 ℃ of the target temperature.