Machine learning-aided inverse design for biogas upgrading through biological CO conversion.

The biogas upgrading process through bioconversion of CO to CH by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH% and H utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant.

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information.

Prediction of biogas production in anaerobic digestion of a full-scale wastewater treatment plant using ensembled machine learning models.

Anaerobic digestion (AD) of sludge is a key approach to recover useful bioenergy from wastewater treatment and its stable operation is important to a wastewater treatment plant (WWTP). Because of various biochemical processes that are not fully understood, AD operation can be affected by many parameters and thus modeling AD processes becomes a useful tool for monitoring and controlling their operation. In this case study, a robust AD model for predicting biogas production was developed using ensembled machine learning (ML) model based on the data from a full-scale WWTP.

Metabolic Mechanism of Sulfadimethoxine Biodegradation by sp. L38 and MASCC-0025.

Antibiotic resistance is one of the most important environmental challenges. Microalgae has been considered as a promising green media for environmental purification. In this work, sulfadimethoxine (SDM) biodegradation potential of sp.

Biodegradation and metabolic fate of thiamphenicol via Chlorella sp. UTEX1602 and L38.

Thiamphenicol (TAP) is a typical medicament in animal husbandry and aquaculture for treating diverse infections. In this work, thiamphenicol biodegradation performance via microalgae was tested. The cultivation results showed that TAP could be biodegraded via the target algae.

Bio-regeneration of different rich CO absorption solvent via microalgae cultivation.

As one of the most mature carbon capture technologies, thermal regeneration of rich CO absorption solvent is a crucial challenge due to its high energy consumption (typically in the range of 3-6 MJ/kg CO). In this work, a concept of bio-regeneration was proposed using microalgae to convert bicarbonate (which is one of the dominant components in rich solution) into value-added biomass. To evaluate the performance of bio-regeneration, different rich solution (including NHHCO, KHCO and NaHCO) were investigated.

Real-time ultrasound elastography for differentiation of benign and malignant thyroid nodules: a meta-analysis.

Objectives: The clinical challenge of managing thyroid nodules nowadays is to diagnose the minority of malignant disease. Real-time ultrasound elastography, which can measure tissue elasticity, is used as a complement to conventional sonography for improving the diagnosis of thyroid tumors. There are 2 common criteria for evaluating an elastogram: the elasticity score and strain ratio.

Macroporous polypyrrole-TiO2 composites with improved photoactivity and electrochemical sensitivity.

Macroporous polypyrrole (PPy)-TiO2 composites were prepared by in situ oxidative polymerization of pyrrole in the macropores of TiO2. The formation mechanism of the PPy nanoparticles, including nucleation and further growth, was proposed by studying the particle growth process with increasing reaction time. The special growth process favors the formation of good cohesion and stabilized interface between the inorganic and organic phases.