LOW COST MUNICIPAL WASTEWATER TREATMENT The present study on low-cost treatment of combined municipal wastewater (MWW) containing food industry effluents by up-flow anaerobic sludge blanket (UASB) in combination with advanced oxidation process (AOP) as post treatment of UASB effluent can be summarized as under: Chemically enhanced primary treatment was first tested in the research to select best coagulant to reduce the pollutant load of MWW. Various coagulants viz. FeCl 3 , Moringa oleifera seed extract and alum were compared for their suitability to treat MWW. Different concentrations (4~32 mg L -1 ) were applied in a series of batch treatment process mode at 600-620 rpm stirring for five minutes. It was observed that alum was effective in reducing the chemical oxygen demand (COD) up-to acceptable level but above the optimized dose of alum total dissolved solids (TDS) were found increasing at greater extent. There was an increase of TDS when the level of alum was increased above the optimized value of 32 mg L -1 . The highest rate of pollutants removal load was observed when 22 to 30 mg L -1 of alum was used. Therefore, alum concentration in the range of 22 to 30 mg L -1 was an optimum dose for MWW treatment. Removal of COD and Ortho-Phosphorus can be possible with increased dose of alum but the cost of alum and increase of TDS shows less relatively favorable. Another low-cost option to compare with chemically enhanced primary treatment (CEPT) was to test waste H 2 O 2 (40%) to treat domestic wastewater, where direct treatment of MWW was given as batch process by 40% waste H 2 O 2 . Waste H 2 O 2 proved as powerful oxidant in minimizing the organic load of MWW. The optimum dose of waste H 2 O 2 was found as 2.5 ml L -1 which significantly (p < 0.05) reduced biological oxygen demand (BOD 5 ) and COD in 120 min, thus meeting the national environmental quality standards (NEQS) for MWW. The results revealed that disinfection capacity of 40% waste H 2 O 2 @ 2.5 ml L -1 caused significant reduction of fecal coliform populations to less than half of the initial value while treating MWW. It was proved most successful and economical removal of COD and microbial load. The most attractive outcome in this regard is using waste from one industry to treat xmunicipal waste. The drawback in this regard, infrequent supply of waste H 2 O 2 from industry can stop the treatment process. Some industries were selected to treat their wastewater. Carwash industry was one of them whose effluent was not studied often. This wastewater contained high content of oil 83 mg L -1 and COD >1000 mg L -1 . Due to presence of oil it was not feasible to treat directly with alum or H 2 O 2 . Therefore aeration was added as pretreatment step to bring oil content to the surface where it was scraped out from aeration tank. Effluent from aeration tank was further treated with alum in second step and H 2 O 2 at third step. Treatment efficiency was 96% oil, COD, turbidity and TDS were reduced upto 93%, 94% and 74%, respectively. The present approach was proved cost effective and requires less space without any pH control. Only costly factor was aeration to separate the oil, other than aeration it can be more expensive to separate. Thus the treatment process can be applied on pilot scale to further evalute its efficiency. The present study also compared the effectiveness of used and fresh H 2 O 2 to treat the domestic waste and to reduce the alum dose for chemical sedimentation. It was observed that used waste 40% H 2 O 2 was very effective and economical. Fresh 35% H 2 O 2 can be purchased Rs 40/L from the market but used waste 40% H 2 O 2 can be obtained only from specific industries where it used for disinfection. Using 40% waste H 2 O 2 was found very effective to reduce COD, turbidity and microbial load. An addition step was merged into H 2 O 2 was ultra violet (UV) light to speed up the reaction. The proposed system was consists of two major step where first sediments was settled down with alum and decant from first step was used to treat with H 2 O 2 and UV in the same tank. This proposed system was effective (p < 0.05) to treat domestic wastewater but carwash and food industry wastewater may require other treatment steps need to be added. The combination of H 2 O 2 with UV light was found very effective (p < 0.05) to decrease BOD, COD, and turbidity and coliform bacteria in MWW. Waste H 2 O 2 generated from an industrial process of disinfection was found more effective in the treatment of domestic wastewater than fresh 35% H 2 O 2 . The waste H 2 O 2 can be applied in combinations with UV light to treat domestic wastewater effectively. The UASB reactor was used to treat mixed MWW at hydraulic retention time 24-48 hrs and at an average temperature 25-34oC. The aim was to test two stage xitreatment concept for low cost MWW treatment as UAB in first stage and waste H 2 O 2 40% 2 ml L -1 of UASB effluent at second stage. Moreover, the effect of micronutrients on the treatability of UAB was also investigated. After start up with glucose for first 15 days (first stage), the reactor was fed with macro and micronutrients synthetic nutrients influent (SNI) for 45 days (second stage). The maximum substrate removal rate was same 0.07 d -1 for both glucose and SNI. Removal efficiency of total suspended solids (TSS), COD, total nitrogen (TN), ortho phosphorus (Ortho-P) and Turbidity as 73%, 99%, 84%, 19% and 67%, respectively. Waste H 2 O 2 was found successful in NH 4+ removal during post treatment where 80% nitrogen was removed. Low cost integrated treatment using UASB and H 2 O 2 was found an excellent novel treatment choice for mixed MWW in developing countries. Anaerobic treatment in combination with post treatment of advanced oxidation process was employed to test the treatability of integrated process for confectionary wastewater mixed with MWW. Reactor was inoculated with 10 year old septic tank sludge and started up with glucose, macro and micro nutrients. The system was operated at 25-30oC, hydraulic retention time (HRT) 48 hrs for 25 days with post treatment with 40% waste H 2 O 2 . This waste H 2 O 2 was collected from industrial process after disinfection of packaging material. Reactor performance was evaluated by pre and post treatment analysis for COD, TSS, TDS, and turbidity and their removal efficiencies were up to 98.6%, 91%, 50% and 81%, respectively. Reactor achieved its full efficiency after feeding diluted wastewater at the value of 1/5, 1/3, 1/2 and 1/0 for 5 – 7 days each. Reactor achieved full efficiency in 25 days removing 98% COD from mixed wastewater. A batch peroxide treatment process was also added to reduce the turbidity and to increase the dissolved oxygen (DO) of treated effluent. The strategy was found quite applicable for the treatment of combined industrial and municipal effluents. Key words: Upflow anaerobic sludge blanket, advanced oxidation process, chemically enhanced primary treatment, national environmental quality standards.
حاجی اسرار احمد ندوۃ المصنفین کے حلقۂ احباب کے لیے اس ماہ المناک سانحہ حاجی اسراراحمد صاحب کی وفات ہے۔اکتوبر۱۹۴۹ء کے آخری سفرکلکتہ میں حاجی صاحب مرحوم سے ملاقات ہوئی تھی اورمیں ان کواچھا خاصا تندرست چھوڑ کرآیا تھا۔ اب عزیزم مولوی سعید احمد کے خط سے اچانک ان کے انتقال کی خبر معلوم ہوئی۔یوں تو دنیا گذشتنی اورگذاشتنی ہے۔یہاں جوآتاہے اُسے ایک نہ ایک دن رخصت بھی ہوجانا پڑتا ہے۔آنے اورجانے کا یہ عمل جب سے دنیا قائم ہے برابر جاری ہے لیکن جانے والوں میں بعض ایسے ہوتے ہیں جواپنے کردار، اخلاق اورعمل کی وجہ سے ایک خاص مقام کے مالک بن جاتے ہیں پھر جب وہ قانون فطرت کے مطابق سفرآخرت اختیار کرلیتے ہیں تو جو جگہ انھوں نے اپنے لیے بنائی تھی وہ خالی محسوس ہونے لگتی ہے۔یہ خلاء رخصت ہوجانے والے کی شخصیت کو یاد دلاتا رہتاہے اوراُس کی مفارقت کااحساس لوگوں میں بڑھ جاتا ہے۔ حاجی اسراراحمد صاحب مرحوم بھی ایسے ہی لوگوں میں سے تھے۔مرحوم آنولہ ضلع بریلی کے باشندہ تھے۔ عرصہ دراز سے کلکتہ میں تجارت کرتے تھے۔ میں چودہ سال ہوئے ان سے کلکتہ میں متعارف ہواتھا۔اس دوران مجھے برابر اُن کے کردار اورعمل کے مطالعہ کاموقع ملتا رہا، وہ صرف اچھے تاجر ہی نہیں بلکہ اپنے دل میں ایک ایساحساس دل بھی رکھتے تھے جس میں مذہب کادرد کوٹ کوٹ کر بھراہواتھا۔ جن کاموں کووہ قوم کے لیے مفید سمجھتے تھے اُن میں اپنی حیثیت سے بھی بڑھ چڑھ کر حصہ لیتے تھے، جب’’ندوۃ المصنفین‘‘کے قیام کا ابتدائی تصور مفتی عتیق الرحمن صاحب کے اورمیرے ذہن میں آیا تو حاجی صاحب مرحوم اس کی تائید کرنے والوں کی صف اوّل میں تھے۔پھرتائید بھی زبانی اور رسمی نہیں بلکہ عملی اور حقیقی، چنانچہ جو تعلق ندوۃ المصنفین سے انھوں نے پہلے دن قائم کیا تھااُسے آخر وقت تک...
Vaccines stability has a major role in the success of immunization programs and saves millions of lives every year. To stabilize vaccines cold chains are developed for storage and transport, as efficiency of vaccines is hampered if they are not kept under proper temperature. Aluminum is used for making vaccine thermostable. The development of vaccine formulation is a critical part of overall development cycle of approving, testing and producing new vaccines. However, Liquid vaccine formulation is still preferred over dry formulation because of ease in using, packaging and manufacturing. Other approaches have been used to make vaccine thermostable. This study demonstrates those processes, used to develop thermo-sensitive vaccines into thermostable vaccine and also describes vaccine formulation designing and use of heat shock protein including mHSP70 and mHSP65 to generate innate and adaptive immune response.
Image clustering deals with the optimal partitioning of images into different groups. Using linear discriminant analysis (LDA) criterion, optimal partitioning of images is obtained by maximizing the ratio of between-class scatter matrix (Sb) to within-class scatter matrix (Sw). In global learning based clustering models, scatter matrices (Sb and Sw) were evaluated on whole image datasets. Owing to which, nonlinear manifold in image datasets may not be effectively handled. For manifold learning, local neighborhood information in data objects were utilized in local learning based clustering models. Further, for high-dimensional data, Sw is singular which corresponds to under-sampled or small-sample-size (SSS) problem of LDA. Owing to which, almost all global learning and local learning based clustering models are based on regularized discriminant analysis (RDA), a variant of LDA. In RDA, the singularity problem of Sw was solved by perturbing it with regularization parameter λ > 0. However, tuning for optimal value of parameter λ is required. Further, for optimal clustering performance in existing state-of-the-art local learning based clustering models, one has to tune a number of clustering parameters from a large candidate set. In this thesis, we propose a novel local learning based image clustering model. Our proposed clustering model is inspired from exponential discriminant analysis (EDA). EDA is another variant of LDA in which SSS problem of LDA was handled using matrix exponential properties. Owing to which, EDA is less parameterized as compared with RDA. Number of nearest neighbor images k is the only clustering parameter in our proposed clustering model as compared with existing state-of-the-art local learning based clustering models. Image clustering performances on 12 benchmark image datasets are comparable over near competitor RDA based image clustering model. Performances are comparable because no discriminant information of LDA is lost in EDA. However, well separated images may not be achieved at local level for image datasets that contain images with pose, illumination, or xiv occlusion variations. Owing to which, local learning based image clustering models may face limitations in such variations. For this problem, various clustering models were proposed in which both global learning and local learning approaches were utilized. However, almost all existing local and global learning based clustering models are based on RDA. Owing to which, tuning of clustering parameters is extensive in almost all state-of-the-art local and global learning based image clustering models. We propose novel local and global learning based image clustering models that are inspired from EDA. Our proposed image clustering models are less-parameterized and computationally efficient where image clustering performances are comparable with existing local and global learning based clustering models. However, performances of all state-of-the-art clustering models are not optimal for challenging image datasets that contain images with illumination and occlusion changes. We explore the challenges in image clustering problem. We show that variation from one image to another image in a class (within-class variation) of an image dataset may vary from nominal to significant due to images with different facial expressions, pose, illumination, or occlusion changes. Using pixel intensity values as image features, we obtain histogram of within-class variation for each image datasets. On the basis of histogram, we categorize image datasets as Gaussian-like or multimodal. We show that image clustering performances of state-of-the-art clustering models are optimal for Gaussian-like image datasets and it degrade significantly for multimodal image datasets. We achieved significant overall performance improvement on 13 benchmark image datasets by employing optimal image descriptors with our proposed clustering model. Our study shows that there is no direct correlation between image clustering performance and local neighborhood structure. However, image clustering performance has correlation with the distribution of within-class variation in image datasets.