Comparison of organic packing materials for toluene biofiltration

he paper focuses on the operation of a pilot plant with four biofilters operated in parallel for determining the suitability of coconut fiber, peat, compost from the digested sludge of a wastewater treatment plant and pine leaves as packing materials for biofiltration of toluene. Physical characteristics of packing materials such as specific surface area, density, pore size and elemental composition were determined for each packing material. Biological activity and packing capabilities related to toluene removal were determined during the startup and operation of the four biofilters under different conditions of nutrients, watering and inlet air relative humidity supply. Nutrient addition was key in improving removal efficiency (RE) and elimination capacity (EC) of biofilters. Feeding of medium with nutrients increased the RE and the EC by a factor of 2 to 4 than these found when supplying only tap water. Additionally, when extra nitrogen was supplied in the medium, RE and EC increased by a factor of 2. Nutrient addition also lead to a microbial population change from bacterial to fungal biofilters. It was denoted that watering control is necessary to improve fungal biofilters performance in terms of ensuring a proper washout of acidic by-products to avoid fungi inhibition and consequent lowered removal capacities.Peer ReviewedPostprint (published version

Characterization of organic packing materials in the removal of ammonia gas in automated biofilters

A fully-automated pilot-scale biofilter filled with coconut fiber as packing material was investigatedfor treatment of ammonia-containing off-gas streams. Coconut fiber was completely characterized forphysical and chemical parameters and biological activity. Biofilter performance was assessed in a pilot-scale unit in a set of continuous experiments varying the inlet ammonia concentration in a range of 45 to240 ppmv at a gas contact time of 36 seconds. Samples taken along the bed height as well as inlet and outletammonia concentrations were used to determine a maximum elimination capacity of 12 g NH3m?3h?1ata 80% removal efficiency. Some features related to nitrification inhibition encountered in the experimentsare also discussed.Peer ReviewedPostprint (published version

A fuzzy-logic based expert system for diagnosis and control of an integrated wastewater treatment

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a lab- scale plant comprising anaerobic/anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. The Fuzzy Logic toolbox for MATLAB was also used for the development of the fuzzy logic rule based system. The fuzzy rules were generated from quantitative and qualitative information, to identify the status of the plant operation and to decide the best commands to be sent to the final control elements to recover the stable operation in case of disturbances of the processes. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 hours. Recycle flow rate from the aerobic to the anoxic module and by-pass flow rate from the influent directly to the anoxic reactor were the output of the fuzzy system that were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognized by the expert system. Denitrification efficiency higher than 85% was achieved 30 hours after the disturbance and 15 hours after the system response at an HRT as low as 1.5 hours. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 hours. The system proved to properly react in order to set adequate operating conditions that timely led to recover efficient N and C removal rates.Fundação para a Ciência e a Tecnologia (FCT) - doctoral research grant BD/1299/2000.União Europeia (UE) - Fundo Social Europeu (FSE) - doctoral research grant BD/13317/2003

Study of NH3 removal by gas-phase biofiltration: effects of shock loads and watering rate on biofilter performance

Ammonia biofiltration performance under shock loads episodes was studied in a reactor packed with coconut fiber as carrier material. Periodical gas and leachate samplings were analyzed and used to characterize the biofilter performance in terms of removal efficiency (RE) and elimination capacity (EC). Nitrogen fractions in the leachate were quantified to identify the experimental rates of nitritation and nitratation.. In a primary experiment a sudden increment of ammonia load was applied for 1 day by changing the ammonia inlet load from 5.2 to 29.1 g N.m-3.h-1. Even though stable operation was obtained (RE of 99.9%), a notable accumulation of nitrite was verified in the leachate. Experimental rates showed that nitritation increased at the same the same ratio that ammonia load was varied. However the nitratation seemed to be largely affected by high ammonia and nitrite concentration. In a subsequent experiment varying the inlet ammonia load, the system was rapidly recovered by increasing the watering rate. Since ammonia was partially removed by physicochemical process as observed in previous experiments, a final experimental was conducted to improve the nitritation capacity. The addition of inorganic carbon source demonstrated to enhance the capacity of the biofilter to degrade a higher amount of ammonia.Peer ReviewedPostprint (published version

Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a lab- scale plant comprising anaerobic/anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 hours. Recycle flow rate from the aerobic to the anoxic module and by-pass flow rate from the influent directly to the anoxic reactor were the output of the fuzzy system that were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognized by the expert system. Denitrification efficiency higher than 85% was achieved 30 hours after the disturbance and 15 hours after the system response at an HRT as low as 1.5 hours. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 hours. The system proved to properly react in order to set adequate operating conditions that timely led to recover efficient N and C removal rates.Fundação para a Ciência e a Tecnologia , Fundo Social Europeu - BD/1299/2000 , BD/13317/2003

Use of Machine Learning in the Function of Sustainability of Wastewater Treatment Plants

Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and prediction approaches of their work. A machine learning tool for development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge to increase its sustainability and helps in its management purposes.Measured data, both in influent and effluent of the WWTP were used for modelling. For the COD model, machine learning tool Weka and algorithm for development of model trees M5P were used.Obtained model has a high descriptive power and correlation coefficient and thus can be used for prediction and modelling purposes, which can help in management and sustainability of the WWTP.Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models

Performance Assessment of Wastewater Treatment Plant with Machine Learning Tools

Uređaji za pročišćavanje otpadnih voda (UPOV) s aktivnim muljem su dinamični i složeni sustavi čije se upravljanje može poboljšati primjenom različitih pristupa modeliranju i predviđanja njihova rada. U ovom radu je korišten alat strojnog učenja (modelska stabla) za izradu modela koncentracije kemijske potrošnje kisika (KPK) na izlazu pročišćene otpadne vode iz UPOV-a s aktivnim muljem. Za modeliranje su korišteni mjereni podaci na ulazu i izlazu otpadne vode iz UPOV-a. U izradi modela koncentracije KPK su korišteni programski alat Weka i algoritam za izradu modelskih stabala M5P. Model dobiven alatom strojnog učenja ima veliku opisnu moć i koeficijent korelacije te se zato može primijeniti u modeliranju koncentracije KPK. Time se u ovom radu ukazuje i na prednosti primjene alata strojnog učenja u izradi modela UPOV-a.Wastewater treatment plants (WWTPs) with activated sludge are complex and dynamic systems whose management can be improved by using different modelling and prediction approaches to their work. A machine learning tool for the development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge. The data measured in both the influent and the effluent of WWTP were used for modelling. For the COD model the machine learning tool Weka and the algorithm for the development of model trees M5P were used. The obtained model has a high descriptive power and correlation coefficient and thus can be used for modelling purposes. Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models