The solid retention time (SRT) is one of the most important parameters for the operation of a wastewater treatment plant (WWTP). Maintaining the SRT at the prescribed set-point is necessary to achieve an appropriate food-to-microorganism (F/M) ratio. This article describes the design and implementation of SRT control at the Ljubljana WWTP. The designed SRT control wastes a defined proportion of the mixed liquor suspended solid (MLSS) mass in the aerobic reactors. The sludge wasted from the secondary settlers is determined based on measurements of the MLSS concentration, waste sludge concentration, and waste sludge flow rate. The SRT control is simple and does not require an SRT estimation or an SRT feedback controller. It includes a constraining controller that keeps the MLSS in the aerobic reactors within the prescribed minimum and maximum values. Validation of the designed SRT control on a mathematical model of the Ljubljana WWTP shows better set-point tracking than the proportional integral (PI) SRT control, as well as reduced SRT variations and lower ammonia nitrogen concentrations compared to the initial MLSS control. The testing of the designed SRT control at the Ljubljana WWTP confirmed the set-point tracking and reduced SRT variations despite large influent variations and operational disturbances.

Wastewater treatment plants (WWTPs) consume a considerable amount of energy. They also generate energy in combined heat and power (CHP) units, which utilise biogas from the anaerobic digestion of sewage sludge to produce renewable electricity. Different prices apply to electricity generated on site in CHP units, to the purchase of electricity from the grid, to the sale of surplus electricity to the grid and energy tariffs, which motivates the optimisation of energy costs. This paper presents a strategy for optimising electricity costs by adapting on-site electricity generation in CHP units to the demand of the WWTP. The approach is designed for a CHP system that generates electricity in multiple internal combustion gas engines. It is implemented as a two-level control system, where the lower control level dynamically adjusts the power of the individual gas engines, and the upper control level optimises the desired total power, taking into account the current energy consumption of the WWTP, biogas reserves and electricity tariffs. The proposed concept was implemented at the Domžale-Kamnik WWTP. A six-month evaluation showed that electricity purchased from the grid could be reduced from 8.7% to 3.3% of the WWTP’s electricity consumption. This reduction affects the system economically, as electricity purchased from the grid at low and high tariffs is 35% and 76% more expensive than electricity generated on site (excluding the grid fee). This approach can be extended to balance dispatchable electricity generation at the WWTP to respond to short-term grid demand.

Wastewater treatment facilities represent intricate systems designed to mitigate the environmental impact of wastewater discharge. However, their operation necessitates substantial energy input. Therefore, it is imperative to ensure both the adequacy of treated water quality and the minimization of energy consumption. In this study, we introduce a methodological framework for developing a neuro-fuzzy model tailored for optimization applications within wastewater treatment plants. We have leveraged this model within a straightforward optimization scheme, implementing it on the standardized BSM2PSFe benchmark model of wastewater treatment plant. Through comparative analysis, our algorithm demonstrates tangible enhancements in performance over the conventional approach currently used in practice. These findings suggest the feasibility of extending our proposed methodology to broader applications across diverse wastewater treatment plant configurations. Given the intricate nature of the BSM2PSFe model and the successful development of a practical model using our proposed approach, it is reasonable to anticipate favorable outcomes upon its application to various real-world wastewater treatment facilities.

Maintaining a high fuel-to-electricity conversion efficiency over a long period of time is important for successful market deployment of SOFC power systems. The conventional control solutions usually do not suffice to reach this goal. In order to properly handle variable load conditions and to mitigate degradation processes, online optimization is needed to adjust the process variables and to run the process as close as possible to the operational optimum. In this paper, a supervisory controller is proposed to optimize the operation of the SOFC power system. The outputs of the supervisory controller are references for the low-level controllers. The optimization is solved by means of the extremum-seeking approach where optimum is sought directly on the process. The main advantage is that the model of the process is not needed. The supervisory controller is assessed on a detailed physical model of a 2.5 kW SOFC power system at different load conditions. Simulation results show that the supervisory controller is capable to improve electrical efficiency and keep the system temperatures within the safe ranges. Consequently, degradation processes associated with the thermal stress of the stack are reduced. Due to the slow dynamics of the stack thermal processes, the convergence rate of the supervisory controller is rather slow. The proposed supervisory controller is thus appropriate for SOFC power systems that operate at constant load conditions over long periods of time.

One of the main challenges for wide-spread utilization of the solid oxide fuel cell (SOFC) power systems is how to achieve high electrical efficiency without increasing the degradation rate of the fuel cells. To run the SOFC power system at high efficiency over a long period of time, properly designed controllers are indispensable. Although a number of various approaches to control SOFC have been proposed so far, it seems that the design of control system, along with simple tuning procedure, has not been treated in a consistent manner. This issue is addressed in the present paper resulting in a feedforward-feedback control structure. The feedforward part is based on the stoichiometry of electro-oxidation, reforming and combustion reactions, which allow immediate response to variable current demand. The feedback part performs additional fine adjustment of fuel and air supply in order to minimize the undesired system temperatures variations. The selection of pairings of manipulated and controlled variables for control is based on physical knowledge of the system. Input/output pairing for single-loop feedback control is assessed by the relative gain analysis. An efficient procedure for tuning the parameters of the feedback controllers is suggested, relying on simple open-loop step responses of the system. The proposed low-level control is assessed on a detailed physical model of a 2.5 kW SOFC power system by simulating two nonstationary load regimes. Simulations show that the control provides a robust operation under large load variations while meeting the operating constraints. Due to its simplicity, the control is feasible for implementation on a real SOFC system. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

The solid oxide fuel cells (SOFCs) represent a promising technology for sustainable power generation from hydrogen rich fuels with high efficiency of energy conversion. However, only a limited number of papers address the problem of on-line maximisation of the efficiency of SOFC operation. Optimal operating conditions are normally chosen either based on experience or by using elaborated models, which are not easy to obtain. Moreover, the process changes over time due to degradation, hence the model-based performance optimisation requires on-line model update. To avoid these problems, a model-free approach is proposed. It is realised in the form of a two-tier control structure where the low-level controllers take over control of the auxiliary units around the fuel cells, while the supervisory controller (SC) controller optimises the operation point of the system. The low-level controllers are conventional feed-forward feed-back controllers, while optimisation on the higher level is solved by using the extremum seeking approach. The proposed control system is demonstrated on a simulated 10 kW SOFC system showing reliable convergence, relative rise of efficiency up to 2% and easy design and maintenance.

This paper presents the design of a plant-wide CNP (carbon-nitrogen-phosphorus) simulation model of a full-scale wastewater treatment plant, which will be upgraded for tertiary treatment to achieve compliance with effluent total nitrogen (TN) and total phosphorus (TP) limit values. The plant-wide model of the existing plant was first designed and extensively validated under long-term dynamic operation. The most crucial step was a precise characterization of input wastewater that was performed by extending the plant performance indicators both to a water line and sludge line and systematically estimating identifiable wastewater characterization parameters from plant-wide performance indicators, i.e. effluent concentrations, biogas and sludge production, and sludge composition. The thus constructed simulation model with standard activated sludge model (ASM2d) and anaerobic digestion model (MantisAD) overpredicted ortho-P and ammonia-N on the sludge line, indicating a need to integrate state-of-the-art physico-chemical minerals precipitation models to simulate plant-wide interactions more precisely. The upgraded plant with multimode anaerobic/anoxic/oxic configuration shows limited denitrification potential. Therefore, additional reject water treatment was evaluated to improve effluent TN and TP performance.

Running SOFC power systems at the edge of efficiency and maximal longevity is important for their successful market deployment. In this paper, we argue that in order to reach this goal, the conventional control solutions do not suffice. In fact, to properly handle variable load conditions and inevitable degradation processes, on-line optimization is needed to adjust the process variables to run the process at the operational optimum. We propose a two-level control system in which the low-level control is upgraded with a supervisory optimizer. The optimization problem targets maximisation of efficiency and minimization of stack temperature violations. It is solved by using the extremum-seeking approach where optimum is sought directly on the process. The results are adjusted references for the low-level controllers. The proposed supervisory optimizer is assessed on a physical model of a 2.5 kW SOFC power system on a standard daily current load profile of residential houses. Simulation results indicate that thanks to the optimizer, efficiency of the system can be improved by about 8% compared to the conventional control scheme with the low-level controllers. Due to its simplicity, the optimizer appears appropriate for practical applications.

Degradation and poisoning of solid oxide fuel cell (SOFC) stacks are continuously shortening the lifespan of SOFC systems. Poisoning mechanisms, such as carbon deposition, form a coating layer, hence rapidly decreasing the efficiency of the fuel cells. Gas composition of inlet gases is known to have great impact on the rate of coke formation. Therefore, monitoring of these variables can be of great benefit for overall management of SOFCs. Although measuring the gas composition of the gas stream is feasible, it is too costly for commercial applications. This paper proposes three distinct approaches for the design of gas composition estimators of an SOFC system in anode off-gas recycle configuration which are (i) accurate, and (ii)easy to implement on a programmable logic controller. Firstly, a classical approach is briefly revisited and problems related to implementation complexity are discussed. Secondly, the model is simplified and adapted for easy implementation. Further, an alternative data-driven approach for gas composition estimation is developed. Finally, a hybrid estimator employing experimental data and 1st principles is proposed. Despite the structural simplicity of the estimators, the experimental validation shows a high precision for all of the approaches. Experimental validation is performed on a 10 kW SOFC system. (C)2017 Elsevier B.V. All rights reserved.

Mathematical models and simulation are becoming increasingly used tools in the optimization of wastewater treatment plants.In this paper, the use of these tools is presented for wastewater treatment plant upgrading.Two case studies are presented, which will be upgraded for tertiary treatment to achieve effluent total nitrogen and total phosphorous concentrations below 10 mg/l and 1 mg/l, respectively.The plant performance after upgrading was assessed by first designing the process model, before upgrading the model for future operation under dynamic influent conditions.Long-term simulations revealed some bottlenecks in the upgraded plant performance and thus helped to improve the plant designs.In one case the total volume of the reactors was increased subsequently, while in the other case tighter denitrification control or additional reject water treatment was proposed.These results indicate that mathematical models can be considered as valuable tools to complement the established wastewater treatment plant design procedures.Advantages are gained by simulating the operation under dynamic operating conditions, precise wastewater characterization, as well as adjustment of stoichiometric and kinetic parameters to a particular wastewater treatment plant operation.

Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option.In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3% error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling.The approach is experimentally validated on a 10 kW SOFC system. (C) 2016 Elsevier B.V. All rights reserved.

The objective of this paper is to show the potential additional insight that results from adding indicators based on Life Cycle Assessment (LCA) to the evaluation criteria of plant performance in the control strategies of wastewater treatment plants. Our assessment combines plant-performance evaluation criteria as effluent quality and operational cost defined by the Benchmark Simulation Model, jointly with a detailed environmental evaluation for impact category provided by LCA. Comparison of control strategies shows that the use of ammonia and carbon controllers provides best effluent quality index, whilst the control strategy that uses ammonia, carbon and storage tank controllers has the lowest values as regards operational cost (the storage tank decrease the operational cost index into a 15%). However, environmental analysis indicates that the control strategy using ammonia, carbon and storage tank controllers generates the lowest environmental impacts in all impact categories except eutrophication; whereas the control strategy using only the carbon controller has the lowest impact on eutrophication (6% lower than the higher control strategies), but the largest impact on the remaining categories (from a 9% higher for terrestrial Ecotoxicity to a 97% higher for the Photochemical oxidation). According to the multiple evaluation criteria results, the control strategy using ammonia, carbon and storage tank controllers is considered as most suitable to implement, highlighting the importance of environmental analysis as an additional source of information for decision makers. The results reported here underline the importance of taking into account integration of the plant performance criteria with environmental evaluation based on LCA for control strategies in wastewater treatment plants.

There is a growing interest within the Wastewater Treatment Plant (WWTP) modelling community to correctly describe physico–chemical processes after many years of mainly focusing on biokinetics. Indeed, future modelling needs, such as a plant-wide phosphorus (P) description, require a major, but unavoidable, additional degree of complexity when representing cationic/anionic behaviour in Activated Sludge (AS)/Anaerobic Digestion (AD) systems. In this paper, a plant-wide aqueous phase chemistry module describing pH variations plus ion speciation/pairing is presented and interfaced with industry standard models. The module accounts for extensive consideration of non-ideality, including ion activities instead of molar concentrations and complex ion pairing. The general equilibria are formulated as a set of Differential Algebraic Equations (DAEs) instead of Ordinary Differential Equations (ODEs) in order to reduce the overall stiffness of the system, thereby enhancing simulation speed. Additionally, a multi-dimensional version of the Newton–Raphson algorithm is applied to handle the existing multiple algebraic inter-dependencies. The latter is reinforced with the Simulated Annealing method to increase the robustness of the solver making the system not so dependant of the initial conditions. Simulation results show pH predictions when describing Biological Nutrient Removal (BNR) by the activated sludge models (ASM) 1, 2d and 3 comparing the performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) treatment plant configuration under different anaerobic/anoxic/aerobic conditions. The same framework is implemented in the Benchmark Simulation Model No. 2 (BSM2) version of the Anaerobic Digestion Model No. 1 (ADM1) (WWTP3) as well, predicting pH values at different cationic/anionic loads. In this way, the general applicability/flexibility of the proposed approach is demonstrated, by implementing the aqueous phase chemistry module in some of the most frequently used WWTP process simulation models. Finally, it is shown how traditional wastewater modelling studies can be complemented with a rigorous description of aqueous phase and ion chemistry (pH, speciation, complexation).

The paper presents a model-based approach to supporting battery selection for a fuel cell (FC)-based auxiliary power unit (APU). It is introduced to a case study of electrical power production and consumption management in a truck anti-idling application of a diesel-powered FC-based APU, a system under development in FCGEN, a FCH JU European project of the FP7 program. With fuel cell and related technologies increasingly competing with others in the market, they need to form complete systems with matching and well-balanced components to enable using the technology to its best. Within the whole system, the battery, serving as an energy buffer, represents a medium-cost element, but it affects the operating parameters importantly. Within the scope of this study, a purpose-oriented model of the diesel powered FC-based system is developed together with a realistic load scenario for the comparison of three batteries. The battery size and type are investigated and discussed in the light of the simulation results. (C) 2014 Elsevier Ltd. All rights reserved.

In this paper we present the application of a modified Extended Kalman filter on a test device. This device is intended to be both rugged and simple to use, providing an accurate position and velocity output for land vehicles. As there are already many applications available for this purpose, our test device is unique in a way that it can be mounted in an arbitrary position on any metal surface on a vehicle, while it automatically discovers its orientation and aligns itself during the first stage of the test period. It comes as an extremely robust and low-cost solution. Furthermore, the pre-alignment outputs are corrected using a reverse output correction during the test period, immediately providing accurate outputs. The alignment algorithm greatly (by factor of 20 or more) reduces the initialization time. In addition, a novel smoothing algorithm with forward computation is described. The developed algorithm is tested with real-world experiments and proved to have a similar accuracy as the reference system, although using much cheaper and less-reliable sensor equipment.