A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater

Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

Machine learning for quantile regression of biogas production rates in anaerobic digesters

Anaerobic digestion is a well-established tool at wastewater treatment plants for processing raw sludge; it can also be used to generate renewable energy by harvesting biogas in anaerobic digesters. Operational parameters, such as temperature, are usually set by plant operators according to expert knowledge. To completely utilize the potential of operational management, in this study, we calibrated a novel Temporal Fusion Transformer based on six years of life-scale time series data together with categorical features such as public holidays. The model design allows for the interpretability of the output in contrast to traditional data-driven techniques, using multi-head attention. In addition to forecasting the median biogas production rates for the following seven days, our model also yields quantiles, making it less prone to strong fluctuations. We used three well-known statistical techniques as benchmarks. The mean absolute percentage error of our forecasting approach is below 8 %.

Experiments and adaptive-network-based fuzzy inference system modelling in a hybrid up-flow anaerobic sludge blanket reactor to assess industrial azadirachtin effluent quality

Experimental investigations were carried out for the treatment of industrial azadirachtin effluent in a hybrid up-flow anaerobic sludge blanket (HUASB) reactor continuously for 115 days in three stages at mesophilic temperature (30 - 35˚C). An adaptive-network-based fuzzy inference system (ANFIS) modelling and statistical regression analysis were applied with the raw data. In the ANFIS modelling as well as in the statistical regression analysis, the operating parameters such as initial pH, influent COD, effluent COD and biogas generation (X₁, X_2, X₃ and X₄) were taken as variables and effluent BOD values as a response (Y). The average percentage error (APE) values of ANFIS modelling were 2.18, 12.29, and 0.01%, for stage-I, II and III respectively. These values indicated that ANFIS modelling performed well in all the three stages and provided more accurate results.

Prediction of methane production from co-digestion of lignocellulosic biomass with sludge based on the major compositions of lignocellulosic biomass

In the present study, the simplex lattice mixture design method was adopted to design the artificial biomass with different ratios of three major components (cellulose, hemicellulose, lignin). The methane yield from the co-digestion of the artificial/ natural biomass (corn stover, wheat stover, rice straw, and peanut stalk) samples with the mixed sludge at the mixture ratio of 1:1 based on total solid (TS) content was recorded for 50 days. The original mathematical prediction models for estimating the cumulative methane production, maximum methane production rate, and lag phase time were established based on the experimental results from the co-digestion of artificial biomass with sludge. To investigate the influence of the structural features of biomass and interactions among the components of biomass which contributing to the inhibition of methane production, the macroscopic factor (MF) was proposed. The mathematical models which revealed the relationship between MF and the methane production parameters were developed by the combination of the prediction results from the original mathematical prediction model and experimental results from the co-digestion of natural biomass with sludge. Modification of the original mathematical prediction models was carried out by considering MF. After modification, the relative error (RE) and root mean square error (RMSE) of the prediction model for cumulative methane production were declined from 19.00 to 30.18% and 42.38 mL/g VS_added to that of - 1.93~7.14% and 4.36 mL/g VS_added, respectively.

Virus removal by membrane bioreactors: A review of mechanism investigation and modeling efforts

The increasing pressure on the global water supply calls for more advanced solutions with higher efficiency and better sustainability, leading to the promptly developing water reclamation and reuse schemes including treatment technologies and risk management strategies where microbial safety is becoming a crucial aspect in the interest of public health. Backed up by the development of membrane technology, membrane bioreactors (MBR) have received substantial attention for their superiority over conventional treatment methods in many ways and are considered promising in the water reclamation realm. This review paper provides an overview of the efforts made to manage and control the potential waterborne viral disease risks raised by the use of effluent from MBR treatment processes, including the mechanisms involved in the virus removal process and the attempts to model the dynamics of the removal process. In principle, generalized and integrated virus removal models that provide insight into real-time monitoring are urgently needed for advanced real-time control purpose. Future studies of approaches that can well handle the inherent uncertainty and nonlinearity of the complex removal process are crucial to the development and promotion of related technologies.

Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes

Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.

Novel start-up process for the efficient degradation of high COD wastewater with up-flow anaerobic sludge blanket technology and a modified internal circulation reactor

To avoid wastage of water resources and operating cost increases caused by the traditional start-up process of large amounts of dilution influent chemical oxygen demand (COD), a novel start-up process (NSP) was developed and verified with water hyacinth juice (WHJ) on an up-flow anaerobic sludge blanket (UASB) and modified internal circulation (MIC) reactor. Results show that UASB and MIC reactors were started successfully and that the MIC reactor exhibited a superior performance. The NSP time of the MIC reactor (46 days) was less than that of the UASB reactor (52 days), although the start-up organic loading rate (OLR) of the MIC reactor was higher than that of the UASB reactor. Interestingly, high-throughput sequencing analysis indicated that the reactor configuration significantly impacted the microbial diversity, however, the UASB and MIC reactors had similar predominant methanogens: Methanosaeta and Methanosarcina. Therefore, acetoclastic methanogenesis is the primary pathway of methane formation during WHJ treatment.

Prediction of greenhouse gas emissions from Ontario's solid waste landfills using fuzzy logic based model

In this study, multi-criteria assessment technique is used to predict the methane generation from large municipal solid waste landfills in Ontario, Canada. Although a number of properties determine the gas generation from landfills, these parameters are linked with empirical relationships making it difficult to generate precise information concerning gas production. Moreover, available landfill data involve sources of uncertainty and are mostly insufficient. To fully characterize the chemistry of reaction and predict gas generation volumes from landfills, a fuzzy-based model is proposed having seven input parameters. Parameters were identified in a linguistic form and linked by 19 IF-THEN statements. When compared to measured values, results of the fuzzy based model showed good prediction of landfill gas generation rates. Also, when compared to other first order decay and second order decay models like LandGEM, the fuzzy based model showed better results. When plotting the LandGEM and Fuzzy model values to the actual measured data, the fuzzy model resulted in a better fit to actual data than the LandGEM model with a coefficient of determination R² of 0.951 for fuzzy model versus 0.804 for LandGEM model. The results show how multi-criteria assessment technique can be used in modelling of complicated processes that take place within the landfills and somehow accurately predicting the landfill gas generation rate under different operating conditions.

Biogas production estimation using data-driven approaches for cold region municipal wastewater anaerobic digestion

The objective of this study was to estimate biogas (including methane, carbon dioxide and hydrogen sulphide) production rates from the anaerobic digesters at the Saskatoon Wastewater Treatment Plant (SWTP), Saskatchewan, Canada. Average daily ambient temperatures typically fluctuate between -40 °C and 30 °C over the year making the management of the SWTP processes challenging. Operating parameters were taken from 2014 to 2016 including volatile fatty acids (VFAs), total solids, fixed solids, volatile solids, pH, and inflow rate. The input parameters were processed using two methods including a correlation test and principal component analysis (PCA) to determine highly correlated variables prior to use in models. The two models used to estimate biogas production rates are a multi-layered perceptron feed forward artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) with grid partition (GP), subtractive clustering (SC) and fuzzy c-means clustering (FCMC). The models using PCA processed variables had reasonable performances with shorter model processing times, while reducing model input data. Among various structures of ANN and ANFIS models for estimation of biogas generation, the ANFIS-FCMC results had better agreement with the observed data. Its average approximation of emission rates of CH₄, CO₂ and H₂S from the wastewater digesters were 3,086, 6,351, and 41.5 g/min, respectively. Our group is assessing similar estimation methodology for the remaining SWTP wastewater treatment processes that are more highly impacted by the seasonal temperature variations including primary and secondary treatment processes.

Investigation of the treatability of molasses and industrial oily wastewater mixture by an anaerobic membrane hybrid system

In this study, the anaerobic treatability of automotive industry wastewater and its treatment in the subsequent membrane system were examined by using molasses, which is a waste of the sugar industry, as a co-substrate. Organic loadings of 3-3, 5-4, and 5gCOD/L/day were applied to a UASB reactor made of steel with a working volume of 7 L. The hydraulic retention time (HRT) was kept constant at 2 days. Temperature, pH, COD, alkalinity, Volatile Fatty acid (VFA) and biogas were monitored. The best COD removal was achieved at the value of 4 gCOD/L/day. The average COD removal rate was 77%. The effluent from the UASB reactor was transferred to the membrane system. The flux reductions of the PW10 kDa UF membrane at different concentrations were 1.717 gCOD/L, 1.934 gCOD/L, 2.257 gCOD/L, 4 gCOD/L, and 8 gCOD/L, and they were 90.78%, 42.69%, 45.88%, 51.00%, and 56.60%, respectively, at the input concentrations. The flux reductions of the UE50 100 kDa UF membrane at the input concentrations of 4 gCOD/L and 8 gCOD/L were 76.00% and 66.25%, respectively. It was determined that the UE50 100 kDa membrane caused more fouling compared to the PW 10 kDa UF membrane. Pore fouling models were determined for the flux reduction in the membranes and the mechanism behind it. Heavy metal and organic matter removals were examined in the effluent obtained from the membrane experiments.

Experimental simulation and fuzzy modelling of landfill biogas production from low-biodegradable MBT waste under leachate recirculation

In the perspective of a sustainable waste management, biodegradable waste destined to landfilling should be reduced. This work aims to study a combination of waste pretreatments and leachate recirculation. A lab-scale experiment and fuzzy-modelling were chosen to predict cumulative methane production from low-biodegradable waste (LBW) under leachate recirculation. Thanks to moisture increase, the degradation of LBW was reactivated and the cumulative methane production reached 28 NL CH₄ kg^-1 after 442 days. The organic fraction was stabilized with a final chemical oxygen demand (COD) of 81 mg L^-1. Fuzzy model was proposed as an alternative to the common deterministic models, affected by high uncertainties. Eleven inputs (pH, Redox potential, COD, volatile fatty acids, ammonium content, age, temperature, moisture content, organic fraction concentration, particle size and recirculation flow rate) were identified as antecedent, and two outputs, or consequents, were chosen: methane production rate and methane fraction in biogas. Antecedents and consequents were linked by 84 IF-THEN rules in a linguistic form. The model was also tested on six literature studies chosen to test different operational conditions and waste qualities. The model outputs fitted the experimental data reasonably well, confirming the potential use of fuzzy macro-approach to model sustainable landfilling.

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Review of Upflow Anaerobic Sludge Blanket Reactor Technology: Effect of Different Parameters and Developments for Domestic Wastewater Treatment

The upflow anaerobic sludge blanket (UASB) reactor has been recognized as an important wastewater treatment technology among anaerobic treatment methods. The objective of this study was to perform literature review on the treatment of domestic sewage using the UASB reactor as the core component and identifying future areas of research. The merits of anaerobic and aerobic bioreactors are highlighted and other sewage treatment technologies are compared with UASB on the basis of performance, resource recovery potential, and cost. The comparison supports UASB as a suitable option on the basis of performance, green energy generation, minimal space requirement, and low capital, operation, and maintenance costs. The main process parameters such as temperature, hydraulic retention time (HRT), organic loading rate (OLR), pH, granulation, and mixing and their effects on the performance of UASB reactor and hydrogen production are presented for achieving optimal results. Feasible posttreatment steps are also identified for effective discharge and/or reuse of treated water.

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200W) as an efficient method and characterized by FE-SEM, FTIR and N₂ adsorption-desorption isotherm. The effect of different parameters such as contact time (0-120min), initial metal ion concentration (25-200mg/L), temperature (298, 318 and 333K), amount of adsorbent (0.002-0.007g) and the solution's initial pH (1-7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R²>0.99). The maximum monolayer adsorption capacity (q_m) of NiO/ACNC was 1428.57mg/L. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R²>0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R²) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor

This paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control system for the prediction of effluent quality and biogas production from an internal circulation (IC) anaerobic reactor system. To improve FNN performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, and a total of 5 rules were extracted in the IF-THEN format. Findings of this study clearly indicated that, compared to NN models, FNN models had smaller RMSE and MAPE as well as bigger R for the testing datasets than NN models. The proposed FNN model produced smaller deviations and exhibited a superior predictive performance on forecasting of both effluent quality and biogas (methane) production rates with satisfactory determination coefficients greater than 0.90. From the results, it was concluded that FNN modeling could be applied in IC anaerobic reactor for predicting the biodegradation and biogas production using paper-mill wastewater.

A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage

A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

Automatic process control in anaerobic digestion technology: A critical review

Anaerobic digestion (AD) is a mature technology that relies upon a synergistic effort of a diverse group of microbial communities for metabolizing diverse organic substrates. However, AD is highly sensitive to process disturbances, and thus it is advantageous to use online monitoring and process control techniques to efficiently operate AD process. A range of electrochemical, chromatographic and spectroscopic devices can be deployed for on-line monitoring and control of the AD process. While complexity of the control strategy ranges from a feedback control to advanced control systems, there are some debates on implementation of advanced instrumentations or advanced control strategies. Centralized AD plants could be the answer for the applications of progressive automatic control field. This article provides a critical overview of the available automatic control technologies that can be implemented in AD processes at different scales.

Modeling of a UASB Reactor by NARX Networks for Biogas Production

An Artificial Neural Network model of a UASB Reactor has been developed. The reactor treats bagasse wash water (containing organics), generated after washing of stored bagasse prior to its use in paper manufacture. In the process, biogas, a renewable source of energy is produced. As the UASB reactors (2×5,000 m3 volume) operate mostly with feed having varying characteristics, therefore a special type of dynamic networks, called NARX networks have been used to model it for predicting biogas production rate. The input to the model is influent flow rate, inlet and outlet COD. Model is based upon 576 days plant data. NARX model architecture consists of input, output, and 2 hidden layers each having 10 neurons and utilizes 4 days delay. The developed ANN model represents the dynamic behavior of UASB reactor and recursively predicts and forecasts the biogas production rate with acceptable deviation with respect to actual production rate.

Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process

Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.

Kinetic modelling and characterization of microbial community present in a full-scale UASB reactor treating brewery effluent

The performance of a full-scale upflow anaerobic sludge blanket (UASB) reactor treating brewery wastewater was investigated by microbial analysis and kinetic modelling. The microbial community present in the granular sludge was detected using fluorescent in situ hybridization (FISH) and further confirmed using polymerase chain reaction. A group of 16S rRNA based fluorescent probes and primers targeting Archaea and Eubacteria were selected for microbial analysis. FISH results indicated the presence and dominance of a significant amount of Eubacteria and diverse group of methanogenic Archaea belonging to the order Methanococcales, Methanobacteriales, and Methanomicrobiales within in the UASB reactor. The influent brewery wastewater had a relatively high amount of volatile fatty acids chemical oxygen demand (COD), 2005 mg/l and the final COD concentration of the reactor was 457 mg/l. The biogas analysis showed 60-69% of methane, confirming the presence and activities of methanogens within the reactor. Biokinetics of the degradable organic substrate present in the brewery wastewater was further explored using Stover and Kincannon kinetic model, with the aim of predicting the final effluent quality. The maximum utilization rate constant U max and the saturation constant (K(B)) in the model were estimated as 18.51 and 13.64 g/l/day, respectively. The model showed an excellent fit between the predicted and the observed effluent COD concentrations. Applicability of this model to predict the effluent quality of the UASB reactor treating brewery wastewater was evident from the regression analysis (R(2) = 0.957) which could be used for optimizing the reactor performance.

Fuzzy-logic modeling of Fenton's strong chemical oxidation process treating three types of landfill leachates

Three multiple input and multiple output-type fuzzy-logic-based models were developed as an artificial intelligence-based approach to model a novel integrated process (UF-IER-EDBM-FO) consisted of ultrafiltration (UF), ion exchange resins (IER), electrodialysis with bipolar membrane (EDBM), and Fenton's oxidation (FO) units treating young, middle-aged, and stabilized landfill leachates. The FO unit was considered as the key process for implementation of the proposed modeling scheme. Four input components such as H(2)O(2)/chemical oxygen demand ratio, H(2)O(2)/Fe(2+) ratio, reaction pH, and reaction time were fuzzified in a Mamdani-type fuzzy inference system to predict the removal efficiencies of chemical oxygen demand, total organic carbon, color, and ammonia nitrogen. A total of 200 rules in the IF-THEN format were established within the framework of a graphical user interface for each fuzzy-logic model. The product (prod) and the center of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed prognostic models. Fuzzy-logic predicted results were compared to the outputs of multiple regression models by means of various descriptive statistical indicators, and the proposed methodology was tested against the experimental data. The testing results clearly revealed that the proposed prognostic models showed a superior predictive performance with very high determination coefficients (R (2)) between 0.930 and 0.991. This study indicated a simple means of modeling and potential of a knowledge-based approach for capturing complicated inter-relationships in a highly non-linear problem. Clearly, it was shown that the proposed prognostic models provided a well-suited and cost-effective method to predict removal efficiencies of wastewater parameters prior to discharge to receiving streams.

Respirometric studies on the effectiveness of biogas production from wastewaters originating from dairy, sugar and tanning industry

The objective of the present study was to determine the effectiveness of biogas production during methane fermentation of wastewaters originating from the dairy, tanning and sugar industries, by means ofrespirometric measurements conducted at a temperature of 35 degrees C. Experiments were carried out with the use of model tanks of volume 0.5 dm3. A high production yield of biogas, with methane content exceeding 60%, was achieved in the case of the anaerobic treatment of wastewaters from the dairy and sugar industries. A significantly lower effect was observed in the case of tanning wastewaters. The effectiveness of the fermentation process decreased with increasing loading of the tanks with a feedstock of organic compounds. By loading a model tank with this feedstock, the effectiveness of treatment ranged from 62.8% to 71.4% residual chemical oxygen demand for dairy wastewaters and from 57.9% to 64.1% for sugar industry wastewaters. The efficiency of organic compound removal from tanning wastewaters was below 50%, regardless of the method applied.

Fuzzy-logic modeling of Fenton's oxidation of anaerobically pretreated poultry manure wastewater

PURPOSE

A multiple inputs and multiple outputs (MIMO) fuzzy-logic-based model was proposed to estimate color and chemical oxygen demand (COD) removal efficiencies in the post-treatment of anaerobically pretreated poultry manure wastewater effluent using Fenton's oxidation process. Three main input variables including initial pH, Fe+2, and H2O2 dosages were fuzzified in a new numerical modeling scheme by the use of an artificial intelligence-based approach.

MATERIALS AND METHODS

Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 70 rules in the IF-THEN format. The product (prod) and the center of gravity (centroid) methods were applied as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two first-order polynomial regression models derived in the scope of this study. Estimated results were also compared to the multiple regression approach by means of various descriptive statistical indicators, such as root mean-squared error, index of agreement, fractional variance, proportion of systematic error, etc.

RESULTS AND DISCUSSION

Results of the statistical analysis clearly revealed that, compared to conventional regression models, the proposed MIMO fuzzy-logic model produced very smaller deviations and demonstrated a superior predictive performance on forecasting of color and COD removal efficiencies with satisfactory determination coefficients over 0.98.

CONCLUSIONS

Due to high capability of the fuzzy-logic methodology in capturing the non-linear interactions, it was demonstrated that a complex dynamic system, such as Fenton's oxidation, could be easily modeled.

Vinasses: characterization and treatments

The final products of the ethanol industry are alcoholic beverages, industrial ethanol and biofuels. They are produced by the same production process, which includes fermentation and distillation of raw materials which come from plant biomass. At the end of the distillation process a waste effluent is obtained called vinasse or stillage. The direct disposal of stillages on land or in groundwater (rivers, streams or lakes), or even for the direct irrigation of crops, pollutes the environment due to their high organic contents, dissolved solids and many other compounds which are toxic or could be contaminants under certain environmental conditions. This work reviews the characterization of vinasses from different feedstock sources and the main treatments for conditioning the soluble solids of vinasses before their disposal.

Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor

The performance of an anaerobic hybrid reactor (AHR) for treating penicillin-G wastewater was investigated at the ambient temperatures of 30-35°C for 245 days in three phases. The experimental data were analysed by adopting an adaptive network-based fuzzy inference system (ANFIS) model, which combines the merits of both fuzzy systems and neural network technology. The statistical quality of the ANFIS model was significant due to its high correlation coefficient R(2) between experimental and simulated COD values. The R(2) was found to be 0.9718, 0.9268 and 0.9796 for the I, II and III phases, respectively. Furthermore, one to one correlation among the simulated and observed values was also observed. The results showed the proposed ANFIS model was well performed in predicting the performance of AHR.