略论啤酒清洁生产

介绍了我国啤酒废水的来源和污染强度，从改革啤酒生产工艺，回收利用废酵母，加强管理，降低酒损等方面论述了啤酒清生产的可行性，通过环境和经济效益分析可以看出，上述措施将在啤酒清洁生产中起到重要作用。     

啤酒废酵母泥的开发利用：液态复合调味品的研制

研究了用氯化钠水溶液从啤酒废酵母泥中提取氨基酸及可溶性物质的最佳与方法，并利用酵母提取液与其它群，香料物质以经例配合 ，制得多种具有厚实风味及强鲜味的调味品。     

啤酒工业清洁生产

介绍我国啤酒废水的来源和污染强度，从改革 啤酒生产工艺、回收利用废酵母。加强管理、降低酒损等 方面论述啤酒清洁生产的可行性。通过环境和经济效 益分析可以看出，上述措施将在啤酒清洁生产中起到重 要作用。     

处理啤酒洗糟废水同时生产单细胞蛋白

用热带假丝酵母在 1.5L气升式内循环生物反应器中处理啤酒生产中的洗糟废水 ,同时生产热带假丝酵母 .探讨了 pH,温度及氮源对降解废水动力学的影响 .实验结果表明 ,用热带假丝酵母既可以有效地降解洗糟废水中的 COD,又可以产生一定数量可作单细胞蛋白的酵母 .对于含糖 0.3%左右的洗糟废水可获得约 500 mg/L(干重 )的酵母 ,并可使废水 COD降解约 80% .     

食品工业废水处理及其资源化

简要叙述酵母前处理技术在处理食品工业废水，尤其含油、高盐分废水中的功效，并分析剩余酵母资源化的可行性。     

接触氧化加气浮处理啤酒废水

一、概述啤酒生产过程中排放的废水是有机废水,含有供微生物生长的丰富物质,其主要来源于: 1.酿造车间的洗罐水,此废水中含有一定量的废酵母、麦糟、热冷蛋白凝固物质等,其废水中COD含量约20000mg/L。 2.包装车间洗瓶、杀菌过程中排放的废水主要含     

浅谈我国啤酒厂废弃物综合利用的现状及发展趋势

随着我国啤酒工业的发展，啤酒生产废弃物（主要是：啤酒废酵母、啤酒糟、污水、污泥，对周围环境的污染正在日益加剧。本文总结和介绍了国内啤酒废弃物综合利用的几种技术比较成熟、经济效益比较显著的方法；以及未来数年内，啤酒废弃物综合利用的目标；对啤酒生产企业进行经营决策和战略决策有很高的参考价值。     

啤酒废酵母菌体变温法自溶的研究

啤酒废酵母菌体自溶技术，是从啤酒废酵母中提取及制取各种生化制品的关键技术。因而，啤酒酵母菌体自溶的改进及提高，对生产实践有重大意义。我们通过发现采用变温法自溶，比国内外比传统的恒温法自溶，不仅自溶程度，收得率及成品品质均有较大的提高，而且自也大大降低，有较高的经济价值及推广价值。     

味精刻处理酵母的选育

由55株酵母选育出了2株耐酸性假丝酵母，摇瓶培养24h，味精废水pH从4．0回升至7．2，CODc ww 45000mg/L降至31000mg/L，去去除31％以上，得到生物量6．5克菌体dL废水，干酵母粉蛋白含量49．4％。     

啤酒废水处理技术及评价

1啤酒废水处理原则啤酒废水主要是啤酒生产工艺中的糖化、发酵、酿造等工序产生的废水，由于啤酒是以大米、大麦为原料，辅之以啤酒花和鲜酵母，经一定的工艺酿造而成，因此，废水中的成份都是具有一定营养价值的物质。据测定，啤酒废水中的主要成份是：麦糟、糖类、果胶、酒花、酵母残渣、蛋白质等有机物和钾、钙、镁的硅酸盐、硫酸盐、磷酸盐等无机物。废水COD值为1000～1500mg／l，BOD5600～1000mg／l,BOD／COD为0．6～0．7，具有很高的可生化性。因此，啤酒废水的处理首先考虑生物法，即遵循“生化法处理为主，物理法或化学法处理…     

废啤酒酵母泥吸附活性黑31的机理研究

探讨了废啤酒酵母泥吸附水中活性黑31的平衡、动力学规律,利用傅里叶红外光谱技术分析了废啤酒酵母泥及活性黑31的吸附官能团间的相互作用.结果表明,废啤酒酵母泥对活性黑31的吸附容易进行;20min前吸附速度都较快,2h后吸附都达到平衡;吸附动力学符合准二级动力学模型.废啤酒酵母泥对活性黑31的吸附等温曲线符合Freund...     

酵母菌对Cr(Ⅵ)的生物吸附作用

研究了啤酒酵母对 Cr( )的吸附作用。当 p H=2时吸附效果最好 ,振荡 30 min吸附基本可达平衡。常见离子Na+、Ca2 +、Mg2 +、Cl-等对吸附影响不大。在一定浓度范围酵母菌对 Cr( )的吸附符合 Freundlich和 Langmuin等温吸附模型。随酵母菌和 Cr( )接触时间的增加 ,Cr( )被还原为 Cr( )。     

多级厌氧系统处理阿卡波糖废渣的中试

阿卡波糖在生产过程中产生大量发酵废渣，目前采用焚烧法进行处理成本高昂，亟须一种环保低廉的处理方法。采用多级厌氧系统对阿卡波糖废渣进行处理，进料负荷达到4.173 kg/（m3·d）时，多级厌氧系统平均总产气量为390.4 m3/d，出料平均可溶性COD为8946 mg/L，系统可将废渣中残余的菌丝体降解完全，废渣固体质量去除率达到52.9%，废渣中残留的阿卡波糖由初始的0.361 mg/g（干渣）下降至0.027 mg/g（干渣），药物残留去除率达到92.5%。且厌氧出料的腐殖化程度大幅提高，实现了对阿卡波糖废渣的减量化和无害化处理。     

合理选择啤酒废水污染治理方案

啤酒行业废水产生量大,CODcr、BOD5浓度高,可生化性强。国内科研单位对啤酒废水进行了长期的实验和实践,取得了很多成功经验。文章介绍了啤酒行业废水产生环节、产生量和水质,针对废水特点,以具体案例分析,说明污染防治措施选择方法,通过先进可靠技术方法和具体项目有机结合,使污染防治措施具有较强的科学性和针对性,从而更好为建设单位、设计部门和环境管理部门提供依据。     

湖泊水库水体氮、磷允许纳污量定量研究

在充分考虑湖库水体污染物实际自净能力、并分析其出流污染物浓度分布规律的基础上,根据风险分析理论和Monte Carlo模拟方法,提出了湖库水体氮、磷允许纳污量计算方法,指出:在满足湖库水体污染物浓度达标率90%以上的条件下,所对应的水体污染物降解量才是湖泊水库水体氮、磷允许纳污量.通过实际算例对水库总氮、总磷允许纳污量的计算,说明了所提出的计算方法的适用性.     

利用酵母菌处理高浓度味精废水的连续小试

利用批量实验对从高浓度味精废水中筛选出的一组酵母菌混合菌群进行了脱氢酶活性(DHA)测试.结果表明用该菌群对COD、硫酸根和氨氮均接近20000mg/L的味精离交尾液进行处理时,其DHA值在前36h高于对离交尾液稀释液的处理,说明高浓度氨氮和硫酸盐对酵母菌活性的影响不显著.利用接种了该酵母菌群的生物接触氧化反应器对味精废水进行的连续小试处理结果表明,COD容积负荷2.0～14.3kg/(m³·d)时,COD去除率稳定在80%以上;适当地补充磷源有利于维持稳定的处理效果对出水pH值和C     

造纸废水综合治理技术的应用研究

采用“酸化 物化 生化”组合工艺并辅以芦苇湿地处理系统对造纸废水进行综合治理。通过优化工艺条件 ,逐级削减污染负荷 ,使废水排放达到国家标准     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Field survey of a sustainable sanitation system in a residential house

Sustainable sanitation is an approach for more ecological and sustainable water resources management. In this paper, we proposed one of the new integrated waste treatment systems： an ＂sustainable sanitation system＂ that includes separation of the black water from water system by a non-flushing toilet （bio-toilet）, and a gray water treatment based on a biological and ecological concept. Sustainable sanitation system also converts the domestic waste to soil conditioners and fertilizers, for farmland use. As one of the case studies, Environmentally Symbiotic Housing in which people actually live using the bio-toilet for the black water treatment and the household wastewater treatment facility for the gray water was introduced. The availability of this system was investigated by analyzing the sawdust used in the bio-toilet and the quality of the effluent in the household wastewater treatment facility. As the result, the water content of the sawdust did not exceed 60% in any of the sampling points and the BOD and COD of the effluent of the household wastewater treatment facility were below 10 and 20 mg/L respectively, due to the low loading. Compared to the pollution load on the water environment created by the conventional system, it was found that the effluent of the house has a lower load than the tertiary treatment and the volume of the water consumption is 75% of the conventional system.