Interval‐Based Diagnosis of Biological Systems – a Powerful Tool for Highly Uncertain Anaerobic Digestion Processes

Anaerobic digestion (AD) is a highly nonlinear time‐varying process commonly used for biological wastewater treatment, which is subject to large disturbances of both influent concentrations, and flow rates that may lead the process to a breakdown. In order to compensate the effect of these disturbances, the dynamics of the main state variables – including biomass – must be closely monitored and used to improve the process performance. However, AD processes still suffer from a lack of reliable and cheap sensors of key process variables to insure the right process operation. This has led to the development of estimation schemes, which infer the information of such key variables from the available measurements. Nevertheless, reliable measurements are not always possible to get because these readings may be corrupted by noise or erroneous due to sensor failures and as a consequence, they may lead to deteriorated control efforts and the eventual crash of the AD process. In this article, we propose an integrated system for the detection, isolation, and analysis of faults in AD processes by using interval observers (IO). The proposed approach was experimentally implemented on a 1‐m³ pilot scale anaerobic digester. Based on the comparison between the measured outputs and their corresponding estimates, results show that this approach was able to detect sensor failures as well as faults in the basic hypotheses made during the design step.     

Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN)

A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS‐X computer program. Then, the mechanistic model was combined with a feed‐forward back‐propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.     

Effect of Different Leachate/Acetate Ratios in a Submerged Anaerobic Membrane Bioreactor (SAnMBR)

Leachate treatment using a membrane bioreactor is an effective method. This study presents a configuration including an anaerobic bioreactor and a membrane module, called submerged anaerobic membrane bioreactor (SAnMBR), for treating influent with leachate/acetate rations (L/A), that were kept to be 10, 25, 50, 75, and 100% at a constant SRT (100 days). COD removal decreased from 85 to 75% when the L/A ratio increased from 10 to 100. To prevent membrane fouling, a SAnMBR was operated in the case of circulation of mixed liquor under continuous and intermittent suction. The average fluxes were 2.60 and 0.40 L/m² h at the periods of intermittent and continuous suction, respectively. The methane production varied between 0.25 and 0.32 L CH₄/g COD_removed.     

Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks

The chemical oxygen demand (COD) parameter of a wastewater treatment plant is predicted based on wavelet decomposition, entropy, and neural networks (NN) for rapid COD analysis. This paper also describes the usage of wavelet and NNs for parameter prediction. Data from a wastewater treatment plant in Malatya, Turkey, were used. This dataset consists of daily values of influents and effluents for a year. To reduce the dimension of input parameters and to decrease the NN training time, wavelet decomposition and entropy were used. Test results were presented graphically. The test results of the trained model were found to be closer to the measured COD values.     

Determination of Real COD in Highly Chlorinated Wastewaters

In order to determine the interference caused by chloride ions in Chemical Oxygen Demand (COD) measurements, synthetically prepared samples with various COD and chloride concentrations were analyzed. In addition, statistical analyses of the results were performed to calculate the real COD caused by the presence of organic substances in chlorinated wastewaters. The results of regression analyses showed good correlation between chloride concentrations and COD levels. A mathematical model is proposed on the basis of the statistical analyses. To calculate the real COD value of a wastewater sample, firstly, the chloride and interfered (or apparent) COD concentrations should be measured and then interference due to the chloride is calculated using the model equation proposed in this study. The real COD value was found by subtracting the COD due to chloride from the apparent COD. The reliability of the proposed model was also validated on real domestic wastewater samples, which were mixed with real seawater in various volumetric ratios to generate a range of salinity concentrations. These real saline samples gave good estimates of real COD with the results indicating that the proposed model can be used with acceptable confidence.     

Disintegration of Sludge by Sonication and Improvement of Methane Production Rates in Batch Anaerobic Digesters

Sludge pretreatment prior to anaerobic digestion has been found to reduce sludge production in wastewater treatment. Sludge disintegration using physical, chemical, biological, or mechanical methods can increase biogas production and reduce sludge quantities. Ultrasonication is one of the most effective means of mechanical disintegration. This study aims to investigate ultrasonication as a means for solubilizing waste activated sludge (WAS) to enhance its digestability. Sonication was applied by the use of two different probes providing different powers and energies into the sludge after which the soluble chemical oxygen demand (sCOD) increases were measured. The samples were then digested anaerobically in 250 mL serum bottles for about 50 days. Along with the biogas measurements, the rate of methane production is calculated to be able to quantify the effect of pretreatment and compare the results between different applications. The results showed that with the increase of sonication power and sonication time, sCOD increased. An introduction of higher sonication energy made the sCOD rise sharply, however, this increase was not indefinite; it became gradually lower with the further increase of energy. The results indicated that specific methane production, specific methane yield, and the first order methane generation rate increased with increasing energy input.     

无机阴离子及溶解性有机质对程海化学需氧量测定值的影响

自2005年以来,程海水体的化学需氧量(COD)持续升高,而生化需氧量(BOD)却维持不变,高锰酸盐指数(COD_(Mn))升高也较缓慢.为研究程海COD持续升高的原因,选取程海水体中具有代表性的无机阴离子(Cl~-、F~-、S~(2-)、HCO_3~-)和溶解性有机质(DOM)中不同浓度的胡敏酸(HA)、富里酸(FA)和商品化腐殖酸(SHA),研究其对COD和COD_(Mn)测定的影响,探讨Cl~-和DOM共同存在下对COD测定的影响.结果表明:程海水体中Cl~-浓度对COD存在显著影响,产生的COD值为5.42 mg/L,S~(2-)、F~-和HCO_3~-对COD影响较小;各离子对COD_(Mn)的影响很小;不同浓度梯度的HA、FA和SHA与COD测定结果呈显著线性相关,氧化1 mg C HA、FA和SHA所产生的COD值分别为2.164、1.964和2.362 mg;氧化1 mg C HA和FA所产生的COD_(Mn)值分别为0.646和0.344 mg;DOM对COD测定值的影响显著大于对COD_(Mn)测定值的影响;且一定浓度Cl~-的存在增强了FA与HA对COD测定的影响.该研究为进一步阐明程海COD逐步升高,COD_(Mn)值缓慢升高的内在原因奠定了基础.     

The Role of Particulate Matter in the Mobilization of Trace Metals during Anaerobic Digestion of Solid Waste Material Die Bedeutung der partikulären Substanz für die Mobilisierung von Spurenmetallen beim anaeroben Abbau fester Abfallstoffe

Metal ions bound to particulate matter represent the greatest portion (i.e. > 95%) of the total metal content found in leachate from reactor experiments where solid waste material was anaerobically digested. This seems true even though strong complexing agents are in solution which increase the solubility of Pb and Cu by a factor up to 10⁴… 10⁵ over that theoretically predicted according to the solubility of the corresponding sulfide mineral. A titrimetric characterization of the metal ion binding sites of the particulate matter suggests that the metal binding properties of the particulate matter are mainly due to organic, aminoacid-type compounds (amines. thio groups, carboxylic groups) probably of bacterial origin. The change of the concentration of the binding sites with time, together with the change of the composition of the particulate matter indicates that bacterial flocs are suspended in the leachate during the switch from acidogenic to methanogenic conditions — either due to the detachment of bacterial films from the solid material by the intensive gas production or due to the formation of syntrophic methanogenic bacterial associations or a combination of both. A combination of the two factors, strong affinity of bacterial mass to metal ions on the one hand, and suspending of the bacterial mass in the leachate on the other hand, will therefore imply a great mobilizing potential for trace metals. Consequently, the highest concentrations of particulate bound Cd were found in reactor experiments where sewage sludge contaminated with Cd was added to the waste material. An increase of the concentration of dissolved cadmium over the solubility of cadmium sulfide, however, could not be observed.