Enhancement effect of zero-valent iron nanoparticle and iron oxide nanoparticles on dark fermentative hydrogen production from molasses-based distillery wastewater

This study investigates the effect of two different iron compounds (zero-valent iron nanoparticle: nZVI and iron oxide nanoparticles: nIO) and pH on fermentative biohydrogen production from molasses-based distillery wastewater. The nZVI and nIO of optimum particle sizes of 50 nm and 55 nm respectively were synthesized and applied for fermentative hydrogen (H₂) production. The addition of nIO & nZVI at (0.7 g/L, pH: 6) resulted in the highest H₂ yield, H₂ production rate, H₂ content and COD reduction. Moreover, the kinetic parameters of H₂ production potential (P) and H₂ production rate (R_m) increased to 387 mL, and 22.2 mL/h, respectively for nZVI, these values were 363 mL and 21.8 mL/h for nIO. The results obtained indicated the positive effect of nZVI and nIO addition on enhanced fermentative H₂ production. The addition of nZVI & nIO resulted in 71% and 69.4% enhancement in biohydrogen production respectively.     

Dark fermentation of starch factory wastewater with acid- and base-treated mixed microorganisms for biohydrogen production

Biohydrogen (Bio-H₂) can be produced from starch factory wastewater and mixed microorganisms using dark fermentation. Acidic and basic chemicals were used to treat the microorganisms to select the hydrogen (H₂)-producing culture. The experiment used a 120 mL bioreactor at 35 °C and the operation commenced with the initial pH level of wastewater in the pH range 4–7 in batch mode. The bacteria:chemical oxygen demand (COD) ratio was 0.2. The initial pH level of the wastewater in the fermentation process affected the H₂ yield and the specific hydrogen production rate (SHPR). For acid-treated bacteria, the maximum H₂ yield and SHPR were produced at an initial pH of 6.5. The maximum H₂ yield and SHPR were 138 mL/g COD degraded and 7.42 mL/g cells?h, respectively. For the base-treated bacteria, the maximum H₂ yield and SHPR were produced at initial pH of 6.5 and pH 7, respectively. The maximum H₂ yield and SHPR were 182 mL/g COD degraded and 25.60 mL/g cells?h, respectively. The COD degradation efficiency levels were 16 and 20% for acid- and base-treated bacteria, respectively. The digested wastewater remained acidic at pH 4.79–4.83. Throughout the study, no methane gas was observed in the gas mixture produced.     

Effect of baking in different ovens on the quality and structural characteristics of saltine crackers

The aim of the study was to evaluate the physical and microstructural characteristics of crackers baked in four different industrial baking ovens (indirect radiation-cyclotherm, indirect convection, hybrid and industrial tunnel-ITO). Indirect convection and cyclotherm ovens provide the highest (5685.43 ± 51 W m⁻²) and the lowest (4860 ± 38.87 W m⁻²) amount of heat flux, respectively. Despite the amount of heat flux, indirect convection led to crackers with the highest moisture (7.86% vs. 4.82% in clyclotherm) and specific volume, but the lowest hardness. Cyclotherm resulted in crackers with lower specific volume, surface area, porosity, smooth and regular surface. Conversely, the hybrid and ITO ovens showed closer heat flux, leading to crackers with similar moisture content, texture parameters, specific volume, browning and inner porosity. Overall results show the potential of baking using different ovens for modifying the quality parameters of the crackers.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.     

新型AMBBR-MBBR-MBR工艺处理紫外线吸附剂厂废水

根据传统工艺处理紫外线吸附剂厂UV-P废水中高级氧化作为预处理成本高,单一A₂O生化工艺出水不稳定,并且出水污染物浓度高等缺点,首先采用零价铁强化水解和预曝气作为预处理,以新型AMBBR-MBBR-MBR工艺进行处理。工程MBBR采用三维多孔泡沫陶瓷填料为生物载体,MBR采用Si O₂平板陶瓷膜。长期调试运行效果表明,出水水质优于≤城镇污水处理厂污染物排放标准≥GB 18918-2002表1中一级A标准,即COD<50 mg/L,氨氮<5 mg/L,总氮<15 mg/L,硝基苯<2.0 mg/L,挥发酚<0.5 mg/L,苯胺类<0.5 mg/L等。水处理运行费用为4.9元/m³。     

脉冲激光与电弧布置方式对铝合金焊接熔滴过渡与焊缝形貌的影响

以2219铝合金为基板，研究了不同脉冲激光-电弧布置方式下的熔滴过渡与焊缝形貌特征，分析了熔深增加的机理. 结果表明，当脉冲激光照射母材时，脉冲激光主要提供对母材的热输入，母材温度的增加有助于促进熔滴铺展，稳定熔滴过渡过程；当脉冲激光照射熔滴缩颈时，主要提供对熔滴的力输入，蒸发反力的作用下形成"一脉一滴"，显著提高熔滴过渡频率与熔滴飞行速度，增加了熔滴对熔池的冲击力，熔深增加；当脉冲激光交替的照射熔池和熔滴时，一方面能够对母材进行加热，有助于熔滴的铺展，另一方面能够提高熔滴过渡频率，提高焊缝的均匀性.     

复合管膜的润湿性及过滤污水机理

为了研究多孔复合管膜对钢铁产业污水的过滤性能及其过滤机理，采用静滴法做了水滴和油滴在复合管膜上的润湿性研究。采用扫描电镜、EDS等手段分析了使用前后的多孔复合管膜外壁及内壁的结构及成分，分析了含油污水中固体颗粒及油的过滤机理，并利用视频光学接触角测量仪测量水滴和油滴在复合管膜上接触角随时间的变化速率。结果表明，在污水过滤过程中，水中小于管膜表层微孔孔径的氧化铁等杂质进入复合管膜内部，无法随水流穿过复合管膜膜层，积累在管膜内部；随着管膜使用时间延长，氧化铁等杂质在复合管膜内部积累量增多。水在复合管膜上的润湿性良好，油滴在复合管膜上是不润湿的。