对“垃圾衍生燃料”RDF之探析

当前,我国水泥工业在可燃废弃物应用技术方面都还处于一家一户、自制自用、效率极低的初级阶段。发达国家的替代燃料:“垃圾衍生燃料”RDF、“固体回收燃料”SRF、“次煤”Subcoal和“纸塑垃圾衍生燃料”RPF制成的原材料都是可燃废弃物,只是处理工艺技术不同或者由垃圾中分拣出的可燃废弃物不同,制成颗粒状衍生燃料的品质不同,这些都可以替代部分甚或替代全部化石燃料在水泥窑炉中应用。我国大力发展“替代燃料”产业,有助于水泥工业消纳更多的“可燃废弃物”,为改善环境尤其是城镇环境和面貌,为我国的节能减排和绿色高质量发展发挥更大的作用。     

肇庆市固体废物产业发展方向探索

本文以肇庆市固体废物产业发展为实例,阐明全市固体废物产业发展现状以及发展方向,并提出了相应的策略。     

Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation

The performance of Microbial electrolysis cell (MEC) is affected by several operating conditions. Therefore, in the present study, an optimization study was done to determine the working efficiency of MEC in terms of COD (chemical oxygen demand) removal, hydrogen and current generation. Optimization was carried out using a quadratic mathematical model of response surface methodology (RSM). Thirteen sets of experimental runs were performed to optimize the applied voltage and hydraulic retention time (HRT) of single chambered batch fed MEC operated with dairy industry wastewater. The operating conditions (i.e) an applied voltage of 0.8 V and HRT of 2 days that showed a maximum COD removal response was chosen for further studies. The MEC operated at optimized condition (HRT- 2 days and applied voltage- 0.8 V) showed a COD removal efficiency of 95 ± 2%, hydrogen generation of 32 ± 5 mL/L/d, Power density of 152 mW/cm² and current generation of 19 mA. The results of the study implied that RSM, with its high degree of accuracy can be a reliable tool for optimizing the process of wastewater treatment. Also, dairy industry wastewater can be considered to be a potential source to generate hydrogen and energy through MEC at short HRT.     

Cold plasma for the disinfection of industrial food-contact surfaces: An overview of current status and opportunities

Food safety is the primary goal for food and drink manufacturers. Cleaning and disinfection practices applied to the processing environment are vital to maintain this safety; yet, current approaches can incur costly downtime and the potential for microorganisms to grow and establish, if not effectively removed. For that reason, manufacturers are seeking nonthermal, online, and continuous disinfection processes to control the microbial levels within the processing environment. One such emerging technique, with great potential, is cold atmospheric pressure plasma (CAP). This review presents the latest advances and challenges associated with CAP-based technologies for the decontamination of surfaces and equipment found within the food-processing environment. It provides a detailed overview of the technology and a comprehensive analysis of the many CAP-based antimicrobial studies on food-contact surfaces and materials. As CAP is considered an emerging technique, many of the recent studies are still in the preliminary stages, with results obtained under widely different conditions. This lack of cohesive information and an inability to directly compare CAP systems has greatly impeded technological development. The review further explores the challenge of scaling CAP technology to meet industry needs, considering aspects such as regulatory constraints, environmental credentials, and cost of use. Finally, a discussion is presented on the future outlook for CAP technology in this area, identifying key challenges that must be addressed to promote industry uptake.     

Adenine-functionalized conjugated polymer as an efficient photocatalyst for hydrogen evolution from water

Conjugated polymers have emerged as a promising class of organic photocatalysts for photocatalytic hydrogen evolution from water splitting due to their adjustable chemical structures and electronic properties. However, developing highly efficient organic polymer photocatalysts with high photocatalytic activity for hydrogen evolution remains a significant challenge. Herein, we present an efficient approach to enhance the photocatalytic performance of linear conjugated polymers by modifying the surface chemistry via introducing a hydrophilic adenine group into the side chain. The adenine unit with five nitrogen atoms could enhance the interaction between the surface of polymer photocatalyst and water molecules through the formation of hydrogen bonding, which improves the hydrophilicity and dispersity of the resulting polymer photocatalyst in the photocatalytic reaction solution. In addition, the strong electron-donating ability of adenine group with plentiful nitrogen atoms could promote the separation of light-induced electrons and holes. As a result, the adenine-functionalized conjugated polymer PF6A-DBTO2 shows a high photocatalytic activity with a hydrogen evolution rate (HER) of 25.21 mmol g^?1 h^?1 under UV-Vis light irradiation, which is much higher than that of its counterpart polymer PF6-DBTO2 without the adenine group (6.53 mmol g^?1 h^?1). More importantly, PF6A-DBTO2 without addition of a Pt co-catalyst also exhibits an impressive HER of 21.93 mmol g^?1 h^?1 under visible light (λ > 420 nm). This work highlights that it is an efficient strategy to improve the photocatalytic activity of conjugated polymer photocatalysts by the modification of surface chemistry.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Changes in polyphenol fractions and bacterial composition after in vitrofermentation of apple peel polyphenol by gut microbiota

The changes in polyphenol fractions after in vitro fermentation of apple peel polyphenol (APP) by gut microbiota as well as the effects of APP on the growth, pH value, short-chain fatty acids (SCFAs) production and intestinal flora composition of gut microbiota fermentation were firstly explored in this study. The relative abundance of Lactobacillus in the APP group was 49.55% with an increment of 49.40% compared with the blank group, while that of Bifidobacterium was 13.32%. Moreover, the flora produced 37.093 ± 0.478 mM of SCFAs including acetic acid, propionic acid, butyric acid and valeric acid during the process of fermentation, thus reduced the environmental pH value. In addition, polyphenol fractions in APP were altered by gut microbiota fermentation to some extent, for example, glycosides were hydrolysed to aglycones. These findings suggested APP as a potential prebiotic agent to alleviate the disorder of intestinal flora.     

我国纺织领域温室气体标准的现状研究

文章从数量、类别、时间等维度描述了我国温室气体标准以及我国纺织领域温室气体标准的发展现状。基于此,简要分析了纺织领域温室气体标准建设存在的问题及原因,提出了未来纺织行业温室气体标准体系建设的建议。     

An infinite chain, {[Ni(tn)2]3[Fe(CN)4(μ-CN)2]2}n,a new catalyst for electrochemical-driven water splitting and photochemical-driven hydrogen evolution from water under blue light

A new catalyst for both water reduction and oxidation, based on an infinite chain, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n, is formed by the reaction of NiCl₂, 1,3-propanediamine (tn) and K₃ [Fe(CN)₆]. {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can electro-catalyze hydrogen evolution from a neutral aqueous buffer (pH 7.0) with a turnover frequency (TOF) of 1561 mol of hydrogen per mole of catalyst per hour (H₂/mol catalyst/h) at an overpotential (OP) of 837 mV {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n also can electro-catalyze O₂ production from water with a TOF of ～45 mol O₂ (mol cat)^?1s^?1 at an OP of 591 mV. Under blue light (λ = 469 nm), together with CdS nanorods (CdS NRs) as a photosensitizer, and ascorbic acid (H₂A) as a sacrificial electron donor, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can photo-catalyze hydrogen generation from an aqueous buffer (pH 4.0) with a turnover number (TON) of 11,450 mol H₂ per mole of catalyst (mol of H₂ (mol of cat)^?1) during 10 h irradiation. The average of apparent quantum yield (AQY) is as high as 40.96% during 10 h irradiation. Studies indicate that {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n exists in two forms: a cyano-bridged chain ({[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n) in solid, and a salt ([Ni(tn)₂]₃ [Fe(CN)₆]₂) in aqueous media; Catalytic reaction occurs on the nickel center of [Ni(tn)₂]²⁺, and the introduction of [Fe(CN)₆]^3- can improve the catalytic efficiency of [Ni(tn)₂]²⁺ for H₂ or O₂ generation. We hope these findings can afford a new method for the design of catalysts for both water reduction and oxidation.