Computational Intelligence Techniques for Control and Optimization of Wastewater Treatment Plants

The development of novel, practice-oriented and reliable instrumentation and control strategies for wastewater treatment plants in order to improve energy efficiency, while guaranteeing process stability and maintenance of high cleaning capacity, has become a priority for WWTP operators due to increasing treatment costs. To achieve these ambitious and even contradictory objectives, this thesis investigates a combination of online measurement systems, computational intelligence and machine learning methods as well as dynamic simulation models. Introducing the state-of-the-art in the fields of WWTP operation, process monitoring and control, three novel computational intelligence enabled instrumentation, control and automation (ICA) methods are developed and presented. Furthermore, their potential for practical implementation is assessed. The methods are, on the one hand, the automated calibration of a simulation model for the Rospe WWTP that provides a basis for the development and evaluation of the subsequent methods, and on the other hand, the development of soft sensors for the WWTP inflow which estimate the crucial process variables COD and NH4-N, and the estimation of WWTP operating states using Self- Organising Maps (SOM) that are used to determine the optimal control parameters for each state. These collectively, provide the basis for achieving comprehensive WWTP optimization. Results show that energy consumption and cleaning capacity can be improved by more than 50%

Nonlinear data driven techniques for process monitoring

The goal of this research is to develop process monitoring technology capable of taking advantage of the large stores of data accumulating in modern chemical plants. There is demand for new techniques for the monitoring of non-linear topology and behavior, and this research presents a topological preservation method for process monitoring using Self Organizing Maps (SOM). The novel architecture presented adapts SOM to a full spectrum of process monitoring tasks including fault detection, fault identification, fault diagnosis, and soft sensing. The key innovation of the new technique is its use of multiple SOM (MSOM) in the data modeling process as well as the use of a Gaussian Mixture Model (GMM) to model the probability density function of classes of data. For comparison, a linear process monitoring technique based on Principal Component Analysis (PCA) is also used to demonstrate the improvements SOM offers. Data for the computational experiments was generated using a simulation of the Tennessee Eastman process (TEP) created in Simulink by (Ricker 1996). Previous studies focus on step changes from normal operations, but this work adds operating regimes with time dependent dynamics not previously considered with a SOM. Results show that MSOM improves upon both linear PCA as well as the standard SOM technique using one map for fault diagnosis, and also shows a superior ability to isolate which variables in the data are responsible for the faulty condition. With respect to soft sensing, SOM and MSOM modeled the compositions equally well, showing that no information was lost in dividing the map representation of process data. Future research will attempt to validate the technique on a real chemical process

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

Real-Time Control of Water Quality and Quantity

The paper considers the application of estimation, forecasting, and control techniques to the problem of combined real-time control of stream discharge and water quality in a river basin. A simple recursive estimation procedure is presented for the on-line estimation of pollutant movement and dispersion in a reach of a river. Some important features of the linear multivariable control system design problem are then considered in the context of controlling downstream discharge and quality given an upstream effluent discharge and surface storage facility as input control variables. Because of the very basic difficulties of visualizing water quality regulation according to most conventional control engineering approaches, a final section of the paper offers a speculative examination of the possibilities for fuzzy control applications in operational river basin management

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Impact of Bioparticle Recirculation in a Circulating Fluidized Bed Biofilm Reactor on Simultaneous Organic and Nitrogen Removal

The Circulating Fluidized Bed Biofilm Reactor (CFBBR), a bioparticle technology designed for biological nutrient removal, has been implemented to achieve considerable biodegradation efficiency and low sludge production, compared with activated sludge system and typical biofilm technology. The inherent advantages of bioparticle technology are enhanced substantially by the CFBBR, for example, decoupling of hydraulic retention time (HRT) from solids retention time (SRT), large specific surface area, ideal conditions for biofilm ecosystem. In this work, bioparticle recirculation, as a novel control method for bioparticle system, was demonstrated in CFBBRs. To verify the impact of bioparticle recirculation on the reactor performance, bio-kinetics and hydrodynamic behavior, three lab-scale CFBBRs were developed and tested for carbon and nitrogen removal from synthetic wastewater as well as municipal wastewater. During all extended experiments, bioparticles are slowly transferred from the Riser (Anoxic column) to the Downer (Aerobic column) for specific bio-reactions, and then recirculating back to the Riser for refreshment. A low shear stress was maintained in order to achieve rich biofilm conditions, where the predation process was encouraged. Furthermore, a novel one-dimensional (1D) bioparticle model successfully combined hydrodynamic parameters with biofilm kinetics to simulate dynamic surface area and dynamic shear stress in bioparticle process. Two lab-scale CFBBRs fed with synthetic wastewater were applied for extended experimental tests and pseudo-steady-state study of bioparticle recirculation. Over the 285 days of synthetic wastewater experiments in a lab-scale (4 L) CFBBR, over 95% COD removal and 85% TN removal were achieved during slow bioparticle circulation between Riser (Anoxic) and Downer (Aerobic). Furthermore, with sodium acetate as the carbon source, an extremely low net sludge yield of 0.034 mgVSS/mgCOD was observed concomitant with the appearance of macro-consumers and aquatic worms. Another extended (200 days) experiment of a lab-scale (8.5 L) CFBBR demonstrated the feasibility of the pseudo-steady-state for integrated COD, nitrogen removal and worm predation, and the results proved that the worm predation has a significant impact on the pseudo-steady-state performance of the CFBBR, decreasing biomass yield and oxygen concentration while increasing expanded bed height. Subsequently, Bioparticle Enrichment-Predation circulation (BEP circulation), comprised enrichment (in Riser Bottom section), transportation (in Riser Top section), predator-cultivation (in Downer Top), and deactivation (in Downer Bottom), was proposed as a novel bioparticle recirculation pattern, which effectively improves performance and enhances stability of CFBBR. The bioparticle process involving worm predation proved to be achievable through a self-balancing worm bioparticle process and BEP circulation, and a self-balancing micro-community along with BEP circulation would provide an effective control of the bioparticle system integrated COD and nitrogen removal as well as strong predation. A CFBBR model was established based on 1D-bioparticle model to investigate hydrodynamic conditions of CFBBR. The model integrated the anoxic riser and aerobic downer, and bioparticle circulation was simulated as a function of expanded bed growth. Experimental data from a 6-L CFBBR fed with municipal wastewater was used to validate the simulation. The CFBBR model can be used to quantify the bed voidage in the riser, and expanded bed height and bed voidage in the downer to achieve good biodegradation performance, optimize the up-flow velocity in both the riser and the downer, then calculate the amount of media for the system. The impact of bioparticle circulation rate (vs) was also studied in the lab, verifyied by three different vs i.e. 50 g bare particle/d, 100 g bare particle/d and 200 g bare particle/d. The range of operational bioparticle circulation rate was calculated by 1D-bioparticle model, which provides crucial control parameter for the CFBBR

Monitoring biological wastewater treatment processes: Recent advances in spectroscopy applications

Biological processes based on aerobic and anaerobic technologies have been continuously developed to wastewater treatment and are currently routinely employed to reduce the contaminants discharge levels in the environment. However, most methodologies commonly applied for monitoring key parameters are labor intensive, time-consuming and just provide a snapshot of the process. Thus, spectroscopy applications in biological processes are, nowadays, considered a rapid and effective alternative technology for real-time monitoring though still lacking implementation in full-scale plants. In this review, the application of spectroscopic techniques to aerobic and anaerobic systems is addressed focusing on UV--Vis, infrared, and fluorescence spectroscopy. Furthermore, chemometric techniques, valuable tools to extract the relevant data, are also referred. To that effect, a detailed analysis is performed for aerobic and anaerobic systems to summarize the findings that have been obtained since 2000. Future prospects for the application of spectroscopic techniques in biological wastewater treatment processes are further discussed.The authors thank the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also acknowledge the financial support to Daniela P. Mesquita and Cristina Quintelas through the postdoctoral Grants (SFRH/BPD/82558/2011 and SFRH/BPD/101338/2014) provided by FCT - Portugal.info:eu-repo/semantics/publishedVersio

Hydrocarbon removal with constructed wetlands

Wetlands have long played a significant role as natural purification systems, and have been effectively used to treat domestic, agricultural and industrial wastewater. However, very little is known about the biochemical processes involved, and the use of constructed treatment wetlands in the removal of petroleum aromatic hydrocarbons from produced and/or processed water. Wastewaters from the oil industry contain aromatic hydrocarbons such as benzene, toluene, ethylbenzene and xylene (ortho, meta and para isomers), which are highly soluble, neurotoxic and cause cancer. The components of the hydrocarbon and the processes of its transformation, metabolism and degradation are complex, the mechanisms of treatment within constructed wetlands are not yet entirely known. This has limited the effective application of this sustainable technology in the oil and gas industries. Sound knowledge of hydrocarbon treatment processes in the various constructed wetlands is needed to make guided judgments about the probable effects of a given suite of impacts. Moreover, most of the traditional treatment technologies used by the oil industry such as hydrocyclones, coalescence, flotation, centrifuges and various separators are not efficient concerning the removal of dissolved organic components including aromatics in the dissolved water phase. Twelve experimental wetlands have been designed and constructed at The King’s Buildings campus (The University of Edinburgh, Scotland) using different compositions. Selected wetlands were planted with Phragmites australis (Cav.) Trin. ex Steud (common reeds). The wetlands were operated in batch-flow mode to avoid pumping costs. Six wetlands were located indoors, and six corresponding wetlands were placed outdoors to allow for a direct comparison of controlled and uncontrolled environmental conditions. The experimental wetlands were designed to optimize the chemical, physical and microbiological processes naturally occurring within wetlands. The outdoor rig simulates natural weather conditions while the indoor rig operates under controlled environmental conditions such as regulated temperature, humidity and light. Benzene was used as an example of a low molecular weight petroleum hydrocarbon within the inflow of selected wetlands. This chemical is part of the aromatic hydrocarbon group known as BTEX (acronym for benzene, toluene, ethylbenzene and xylene), and was used as a pollutant together with tap water spiked also with essential nutrients. The study period was from spring 2005 to autumn 2007. The research focused on the advancing of the understanding of biochemical processes and the application of constructed wetlands for hydrocarbon removal. The study investigated the seasonal internal interactions of benzene with other individual water quality variables in the constructed wetlands. Variables and boundary conditions (e.g. temperature, macrophytes and aggregates) impacting on the design, operation and treatment performance; and the efficiency of different wetland set-ups in removing benzene, chemical oxygen demand (COD), five-day @ 20°C N-Allylthiourea biochemical oxygen demand (BOD5) and major nutrients were monitored. Findings indicate that the constructed wetlands successfully remove benzene (inflow concentration of 1 g/l) and other water quality variables from simulated hydrocarbon contaminated wastewater streams with better indoor (controlled environment) than outdoor treatment performances. The benzene removal efficiency was high (97-100%) during the first year of operation and without visible seasonal variations. Seasonal variability in benzene removal was apparent after spring 2006, the highest and lowest benzene removal efficiencies occurred in spring and winter, respectively. In 2006, for example, benzene removal in spring was 44.4% higher than in winter. However, no seasonal variability was detected in the effluent ammonia-nitrogen (NH4-N), nitratenitrogen (NO3-N) and ortho-phosphorus-phosphate (PO4 3--P) concentrations. Their outflow concentrations increased or decreased with corresponding changes of the influent nutrient supply. In addition, benzene treatment led to trends of decreasing effluent pH and redox potential (redox) values but increasing effluent dissolved oxygen (DO) concentrations. Approximately 8 g (added to the influent every second week) of the well balanced slow-releasing N-P-K Miracle-Gro fertilizer was sufficient to treat 1000 mg/l benzene. Results based on linear regression indicated that the seasonal benzene removal efficiency was negatively correlated and closely linked to the seasonal effluent DO and NO3-N concentrations, while positively correlated and closely linked to the seasonal effluent pH and redox values. Temperature, effluent NH4-N and PO4 3--P concentrations were weakly linked to seasonal benzene removal efficiencies. During the entire running period, the seasonal benzene removal efficiency reached up to 90%, while the effluent DO, NO3-N, pH and redox values ranged between 0.8 and 2.3 mg/l, 0.56 and 3.68 mg/l, 7.03 and 7.17, and 178.2 and 268.93 mV, respectively. Novel techniques and tools such as Artificial Neural Network (self-organizing map (SOM)), Multivariable regression and hierarchical cluster analysis were applied to predict benzene, COD and BOD, and to demonstrate an alternative method of analyzing water quality performance indicators. The results suggest that cost-effective and easily to measure online variables such as DO, EC, redox, T and pH efficiently predicted effluent benzene concentrations by applying artificial neural network and multivariable regression model. The performances of these models are encouraging and support their potential for future use as promising tools for real time optimization, monitoring and prediction of benzene removal in constructed wetlands. These also improved understanding of the physical and biochemical processes within vertical-flow constructed wetlands, particularly of the role of the different constituents of the constructed wetlands in removal of hydrocarbon. These techniques also helped to provide answers to original research questions such as: What does the job? Physical design, filter media, macrophytes or micro-organisms? The overall outcome of this research is a significant contribution to the development of constructed wetland technology for petroleum industry and other related industrial application