Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability.

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

Iron driven organic carbon capture, pretreatment, recovery and upgrade in wastewater: Process technologies, mechanisms, and implications.

Wastewater treatment plants face significant challenges in transitioning from energy-intensive systems to carbon-neutral, energy-saving systems, and a large amount of chemical energy in wastewater remains untapped. Iron is widely used in modern wastewater treatment. Research shows that leveraging the coupled redox relationship of iron and carbon can redirect this energy (in the form of carbon) towards resource utilization. Therefore, re-examining the application of iron in existing wastewater carbon processes is particularly important. In this review, we investigate the latest research progress on iron for wastewater carbon flow restructuring. During the iron-based chemically enhanced primary treatment (CEPT) process, organic carbon is captured into sludge and its bioavailability is enhanced through iron-based advanced oxidation processes (AOP) pretreatment, further being recovered or upgraded to value-added products in anaerobic biological processes. We discuss the roles and mechanisms of iron in CEPT, AOP, anaerobic biological processes, and biorefining in driving organic carbon conversion. The dosage of iron, as a critical parameter, significantly affects the recovery and utilization of sludge carbon resources, particularly by promoting effective electron transfer. We propose a pathway for beneficial conversion of wastewater organic carbon driven by iron and analyze the benefits of the main products in detail. Through this review, we hope to provide new insights into the application of iron chemicals and current wastewater treatment models.

Robust Estimation of Heterogeneous Treatment Effects: An Algorithm-based Approach.

Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators' robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents.