Choice of sewage sludge thermochemical disposal methods from multi‐ perspective analysis

Thermochemical conversion disposal methods for sewage sludge usually include incineration, gasification and pyrolysis. Incineration technology is relatively mature and the incineration ash can be potentially used for phosphorus (P) recovery. Gasification can be used to recover syngas which is convenient to be used for power & heat generation. While through pyrolysis, syngas of high quality, tar and char can be recovered. To make a proper choice from them, these techniques are compared from perspective of technology maturity, investment, operation cost, environmental impact and acceptability of the public. Technology maturity is evaluated by comparing industrial applications. Investment and operation cost are evaluated based on practical operation experiences; environmental impacts are evaluated based on life cycle assessment; and acceptability of the public is based on a questionnaire survey. Based on a scenario with capacity of 100 t/d in eastern China, investment are comparable for the three technologies within the range of 250,000-400,000 RMB yuan/(ton.d) with gasification close to the higher side; the operation cost varies in the range of 140 - 400 RMB yuan/ton with incineration the highest; pyrolysis corresponds to the lowest environmental impacts and the highest acceptability of the public, however the pyrolysis technology is not fully developed, especially the durable pyrolysis reactor and the application of pyrolysis char

MSW derived syngas utilization in combination with distributed energy supply system: mode selection and evaluation

Syngas from MSW gasification and pyrolysis are characterized through a rotary kiln reactor followed by a gas scrubber, the components and energy & chemicals for cleaning the syngas were qualified. Then the syngas utilization modes in combination with distributed energy supply system are evaluated. For the separated MSW sample with dry-basis higher heat value (HHV) of 15.23MJ/kg, the HHV of pyrolysis syngas is higher than 15 MJ/kg. When combined with distributed energy supply system, the syngas is cleaned first to remove dust, tar and harmful components such as NH3 and H2S, then is used to supply power & heat. Based on a downtown district in a northern city in China, the syngas from pyrolysis of 43.34 ton/d separated MSW is assumed to generate power & heat through 4 routes, as shown in Fig.1: l burning in a gas engine (1); l burning in a gas turbine (2); l direct combustion in a furnace to generate steam followed by a steam turbine (3); l undergoing separation to obtain pure CH4 or H2 for industrial applications as avoidance of pure gas production or utilization in fuel cell (4). Please click Additional Files below to see the full abstract

Optimization of waste disposal method in urban functional zone based on multiple model evaluation: a case study of an urban sub‐domain planning

With the implements of urban and rural areas integration planning and new rural countryside construction, a new town planning is springing up in China. Planning of municipal solid waste (MSW) disposal is one of the most important contents. In the new town planning, MSW is considered as one kind of energy source instead of a trash stream, so how to choose a suitable method of waste to energy (WtE) and analyze the feasibility of the method combined with urban functional zone need to be considered. In this paper, by adopting the analytical hierarchy process (AHP) method which consists of life cycle analysis (LCA) model, cost benefit analysis (CBA) and questionnaire survey, the environmental benefits, economic benefits and social benefits are considered to decide the best WtE route for MSW in an urban functional zone. The functional unit is selected to be 1 ton of waste, and the system boundaries include the process of collection and transportation, disposal, and utilization of generation and treatment of residue. The alternatives include central incineration, in situ pyrolysis, central incineration of combustible MSW+ in situ anaerobic digestion of organic waste and in situ pyrolysis of combustible MSW + in situ anaerobic digestion of organic waste. The 4 alternatives are evaluated to determine the best suitable disposal method of MWS of a center business district (CBD) of a city in north China. The area of the CBD is 3150m×2100m, the generation rate of MSW is about 78t/d, of which the proportion of food waste is 50.84%. For small and moderate scale, tubular reactor can be competitive, so tubular reactor is selected in this study. The technical routes of the 4 alternatives are as followed: Collection (dry waste & wet waste) ®transportation (78t/d) ®central incineration (entire city: 836t/d) (energy generation efficiency: 25%; flue gas: purification; bottom ash: landfill; fly ash: solidification and landfill) Collection (dry waste & wet waste)®pyrolysis in transfer station (78t/d) ® pyrolysis gas into distributed energy station (pyrolysis gas: purification; residual char: landfill) Collection (dry waste)®transportation(38.3t/d)®central incineration(entire city: 836t/d) Collection(wet waste)®anaerobic digestion in transfer station(39.6t/d) ®biogas into distributed energy station (biogas: purification; digestion residue: landfill) Collection(dry waste)®pyrolysis in transfer station(38.3t/d)®pyrolysis gas into distributed energy station(pyrolysis gas: purification; residual char: landfill) Collection(wet waste)®anaerobic digestion in the transfer stations (39.6t/d) ®biogas to distributed energy station (biogas: purification; biogas residue: landfill) The evaluation result shows that the alternative 4 is the most suitable disposal method in urban functional zone

Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations

Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit user feedback such as user reviews. It however remains under-explored how the extracted aspects can help generate more meaningful recommendations to the users. Meanwhile, existing research on aspect-based recommendations often relies on separate aspect extraction models or assumes the aspects are given, without accounting for the fact the optimal set of aspects could be dependent on the recommendation task at hand. In this work, we propose to combine aspect extraction together with aspect-based recommendations in an end-to-end manner, achieving the two goals together in a single framework. For the aspect extraction component, we leverage the recent advances in large language models and design a new prompt learning mechanism to generate aspects for the end recommendation task. For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model. The recommendation task mediates the learning of the user embeddings and item embeddings, which are used as soft prompts to generate aspects. Therefore, the extracted aspects are personalized and contextualized by the recommendation task. We showcase the effectiveness of our proposed method through extensive experiments on three industrial datasets, where our proposed framework significantly outperforms state-of-the-art baselines in both the personalized aspect extraction and aspect-based recommendation tasks. In particular, we demonstrate that it is necessary and beneficial to combine the learning of aspect extraction and aspect-based recommendation together. We also conduct extensive ablation studies to understand the contribution of each design component in our framework

Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems

Recommending novel content, which expands user horizons by introducing them to new interests, has been shown to improve users' long-term experience on recommendation platforms \cite{chen2021values}. Users however are not constantly looking to explore novel content. It is therefore crucial to understand their novelty-seeking intent and adjust the recommendation policy accordingly. Most existing literature models a user's propensity to choose novel content or to prefer a more diverse set of recommendations at individual interactions. Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity. To this end, we propose a novel hierarchical reinforcement learning-based method to model the hierarchical user novelty-seeking intent, and to adapt the recommendation policy accordingly based on the extracted user novelty-seeking propensity. We further incorporate diversity and novelty-related measurement in the reward function of the hierarchical RL (HRL) agent to encourage user exploration \cite{chen2021values}. We demonstrate the benefits of explicitly modeling hierarchical user novelty-seeking intent in recommendations through extensive experiments on simulated and real-world datasets. In particular, we demonstrate that the effectiveness of our proposed hierarchical RL-based method lies in its ability to capture such hierarchically-structured intent. As a result, the proposed HRL model achieves superior performance on several public datasets, compared with state-of-art baselines