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Several studies showed that some organic micropollutants (OMPs) are biotransformed during anaerobic digestion (AD). Yet, most of them aim at reporting removal efficiencies instead of understanding the biotransformation process. Indeed, how each of the main AD stages (i.e., hydrolysis, acidogenesis, and methanogenesis) contribute to OMP biotransformation remains unknown. This study focuses on investigating the role of methanogenesis, the most characteristic step of AD, to OMP removal. More specifically, the sorption and the biotransformation of 20 OMPs by methanogenic biomass were analyzed determining their concentrations in both liquid and solid phases. Sorption onto methanogenic biomass displayed a similar behavior as reported for digested sludge. Most of the OMPs were biotransformed to a medium extent (35–70%) and only sulfamethoxazole was completely removed. Comparing these results with those reported for the complete AD process, methanogenesis was proven to play a key role, accounting for more than 50% of the OMP biotransformation (except for roxithromycin) during AD. An increase in the organic loading rate from 1 to 2 g COD/L d, typical loads employed in sewage sludge anaerobic digesters, did not exert a clear cometabolic effect on the OMPs biotransformation. It is hypothesized that biotransformation occurs in both liquid and solid phases because no link between the partition coefficient (Kd) and the overall biotransformation efficiency was found. These findings allow a better understanding of the OMPs fate under anaerobic conditions, which is necessary to design efficient biological mitigation strategies

The stoichiometric relations between substrate and product are the first parameter obtained for novel processes. We propose here a method for process monitoring that uses the stoichiometry matrix and a linear least square optimisation as a simple approach for process monitoring. The new method is benchmarked versus a well-established method: the continuous-discrete extended Kalman filter (CD-EKF) and both are compared estimating biological rates of a novel technology. It is seen that, although inferior to the CD-EKF, the new method can be an alternative for process monitoring in wastewater treatment

Mixed culture fermentations (MCFs) are recognised as an inexpensive means to produce high-added-valueproducts from low-grade biomass. However, developing a new bioprocess based on this technology is a challenging task. Although mixed cultures are advantageous when treating complex substrates in a continuous operation, they also pose a fundamental challenge: we are not able to fully understand the mechanisms that control these populations. In consequence, it is difficult to control the operation and to foresee the outcome of the process. In this context, BIOCHEM project (Figure 1) aims at designing a methodology for the development of a novel process based on MCFs focusing on two aspects: reaching a high productivity and achieving a high selectivity of the desired product(s)

Mixed culture fermentations (MCFs) are recognised as an inexpensive means to produce high-added-valueproducts fromlow-grade biomass However, developing a new bioprocess based on this technology is a challenging task. Although mixed cultures are advantageous when treating complex substrates in a continuous operation they also pose a fundamental challenge: they are complex and uncharacterized and we are not able to fully understand the mechanisms that control these populations. In consequence, it is difficult to control the operation and to foresee the outcome of the process. In this context, we present ERA-NET IB project BIOCHEM project aiming at designing a methodology for the development of a novel process using MCFs focusing on two aspects: reaching a high productivity and achieving a high selectivity of the desired product(s)

In response to strong growth in energy intensive wastewater treatment, public agencies and industry began to explore and implement measures to ensure achievement of the target indicated in the 2020 Climate and Energy Package. However, in the absence of fundamental and globally recognized approach evaluating wastewater treatment plants (WWTPs) energy performance, these policies could be economically wasteful. This paper gives an overview of the literature of WWTPs energy-use performance. Energy key performance indicators (KPIs) found are presented and critically assessed, pointing out the limits to their validity. Data from more than 430 WWTPs, together with the methods for synthesizing the information is presented. The assessment of a large data sample provided some evidence about the effect of the plant size, dilution factor and flowrate. The technology choice, plant layout and country of location were seen as important elements that contributed to the large variability observed

The energy efficiency of wastewater treatment plants (WWTPs) is a subject of growing interests by water utility and WWTPs operators. As consequence, benchmarking in recent years has become an important tool to assess the performance of these facilities and help identify best practices. This study compares various benchmarking methodology employed for the assessment of the energy efficiency in WWTPs. The literature review revealed three main benchmarking approaches: normalization, statistical techniques and programming techniques. Advantages and disadvantages were identified for each one. It was found additionally that no single Key Performance Indicators (KPIs) used to characterize the energy performance could be used universally. In general, while these methods can be used for comparison, the diagnosis of the energy performance remains an unsolved issue

The determination of diffusivity of compounds that can be transferred from packaging into food products is a keystone to ensure consumer’s safety. However, no clear guidelines exist as how the diffusion experiment should be designed in order to maximise the accuracy of the estimated parameter. With the perspective of optimal experimental design, a methodology to estimate the diffusivity of a migrating compound in a polymer is presented, both for global methods (that measure the overall concentration) and local methods (that measure the profile of concentration along the polymer thickness). To demonstrate the methodology, real experimental data (transfer of Uvitex OB from linear low-density polyethylene) are used, and the OED based methodology is benchmarked against other heuristics. It is seen that, although the OED methodology outperforms the rest of methodologies when good initial guesses are available, its performance becomes deteriorated when gross over- or underestimations of the true value are made

In response to strong growth in energy intensive wastewater treatment, public agencies and industry began to explore and implement measures to ensure achievement of the targets indicated in the 2020 Climate and Energy Package. However, in the absence of fundamental and globally recognized approach evaluating wastewater treatment plant (WWTP) energy performance, these policies could be economically wasteful. This paper gives an overview of the literature of WWTP energy-use performance and of the state of the art methods for energy benchmarking. The literature review revealed three main benchmarking approaches: normalization, statistical techniques and programming techniques, and advantages and disadvantages were identified for each one. While these methods can be used for comparison, the diagnosis of the energy performance remains an unsolved issue. Besides, a large dataset of WWTP energy consumption data, together with the methods for synthesizing the information, are presented and discussed. It was found that no single key performance indicators (KPIs) used to characterize the energy performance could be used universally. The assessment of a large data sample provided some evidence about the effect of the plant size, dilution factor and flowrate. The technology choice, plant layout and country of location were seen as important elements that contributed to the large variability observed

Operation of wastewater treatment plants can be subjected to economic, energetic and/or environmental objectives, besides the compliance with effluent limits. As trade-offs between different objectives are frequently unavoidable, model based analysis can assist in decision making and give further insight on the effect of the operating conditions. Furthermore, as new wastewater treatment technologies have appeared in the latest years, model-based analysis is needed to ascertain what the advantages of the new technologies are. We demonstrate here how to assess and operate an innovative WWTP according to different objectives with case-study based on a real innovative pilot plant. The plant features the use of denitrifying anaerobic methane oxidation (DAMO) bacteria to deplete methane from digestate. Furthermore, given the slow growth rate of the system and the tendency to create complex syntrophic environments, the use of a model becomes a keystone to operate these reactors.

Wastewater treatment plants are relevant energy consumers. As they carry out complex operations submitted to large variability caused by, among others, the influent and loading variations, the assessment of the performance is challenging. In this study we perform a regression analysis over a sample of 185 WWTP data in order to establish a regression model that predicts the average energy consumption of the plant given plant design and operation predictors such as the average influent flowrate, the COD loading and the plant loading factor. The regression model is also used to ascertain whether the location of the WWTP in France, Spain and Germany has an impact over the energy consumption

Waste Water Treatment Plants (WWTPs) account for more than 1% of consumption of electricity in Europe, one of the largest shares of energy use among public industries. The aim of ENERWATER is to provide measuring tools to quantify the energy consumption of WWTPs and to elaborate the standards to compare and ultimately optimise the operation of WWTPs. The methodology is demonstrated on 50 WTTPs, whose energy consumption is thoroughly monitored. ENERWATER goals will be reached by i) defining the concept of energy efficiency in WWTP and the performance indicators suitable for its quantification; ii) standardising the methods for measuring energy consumption in order to ensure that comparable figures are obtained; iii) using data treatment tools to not only quantify but also diagnose the reasons for energy inefficiency and iv) proposing a global index that measures the energy efficiency of a WWTP. All these steps will eventually be part of a general methodology that can be the draft of a standard for measuring energy efficiency in WWTP. ENERWATER is a three-year activity that involves 9 partners (universities and companies) from 4 European countries (Germany, Italy, Spain and the United Kingdom). To ensure a fast transfer of results to the relevant actors, ENERWATER puts in contact research groups, SMEs, utilities, city councils, policy makers and industry beyond the project consortium. These actions should bring European Water Industry a competitive advantage in new products development and a faster access to markets by facilitating evidence of energy reduction, thereby fostering adoption on new technologies.

A systematic design of an optimal control structure for the SHARON-Anammox nitrogen removal process is studied. The methodology incorporates two novel features to assess the controllability of the design variables candidate for the regulatory control layer: (i) H- control method, which formulates the control problem as a mathematical optimization problem, and (ii) close-loop disturbance gain (CLDG) plots. It is shown that the methodology is especially appropriate for bioreactors. The solution of the mixed sensitivity stacked H control problem ranked the combinations of controlled variables (CVs). The best candidates to CVs were paired with the manipulated variables using the relative gain array. The proposed control structure was further analyzed and verified for disturbance rejection using the CLDG plots. The optimal pairing of CVs with the actuators (kLa and acid/base addition) is found to be dissolved oxygen (DO) and pH in the SHARON reactor. Furthermore, to relate the controller actions to process operation objective, nitrogen removal efficiency, two cascade control systems are designed. The first cascade loop controls TNN/TAN ratio in the influent to the Anammox reactor by adjusting the set point for DO in the regulatory layer, while the second cascade loop controls the nitrogen removal efficiency (i.e. effluent TNN and TAN) by adjusting the TNN/TAN ratio at the effluent of the SHARON reactor. The control system is evaluated and benchmarked using a set of realistic dynamic scenario simulations, demonstrating that the different control strategies successfully maintain stable and high nitrogen removal efficiency. The nested cascade control structure shows the best performance, removing up to 95% of the influent ammonia. Both the control design methodology and the resulting optimal control structures are expected to contribute to stable operation and control of these emerging nitrogen removal technologies.

Complete Autotrophic Nitrogen Removal (CANR) is a novel process where ammonia is converted to nitrogen gas by different microbial groups. The performance of the process can be compromised by an unbalanced activity of the biomass caused by disturbances or non-optimal operational conditions. This contribution describes the development of a fuzzy-logic based system for both diagnosis and control of a CANR reactor. Based on a combination of measurements of the nitrogen species concentration in the influent and in the effluent on the one hand, and insights into the activities of three distinctive microbial groups on the other hand, the diagnosis provides information on: nitritation, nitratation, anaerobic ammonium oxidation and overall autotrophic nitrogen removal. These four results give insight into the state of the process and are used as inputs for the controller that manipulates the aeration to the reactor.The diagnosis tool was first evaluated using 100 days of real process operation data obtained from a lab-scale single-stage autotrophic nitrogen removing reactor. This evaluation revealed that the fuzzy logic diagnosis is able to provide a realistic description of the microbiological state of the reactor with process engineering insight analysis. An evaluation of both the diagnosis tool and the controller was done by simulating a disturbance in the influent concentration. High and steady nitrogen removal efficiency was achieved thanks to the diagnosis and control system. Finally, development of the diagnosis and control as two independent systems provided further insight into the operation performance, gives transparency towards the operator and makes the system flexible for future maintenance or improvements.

A novel feedforward–feedback control strategy was developed for complete autotrophic nitrogen removal in a sequencing batch reactor. The aim of the control system was to carry out the regulation of the process while keeping the system close to the optimal operation. The controller was designed based on a process model and then tested experimentally. The resulting batch-to-batch control strategy had the total nitrogen removal efficiency as controlled variable and the setting of the aeration mass flow controller as manipulated variable. Compared to manual operation mode (constant air supply), the controller resulted in a significant performance improvement: removal efficiency was kept at a stable high level in the presence of influent ammonium concentration disturbances, and the absolute deviation on removal efficiency was reduced by 40%. The successful validation of the controller in a lab-scale reactor is a promising result, which brings this control strategy one step closer to full-scale implementation.

A pH simulator consisting of an efficient numerical solver of a system of nine nonlinear equations was constructed and implemented in the modeling software MATLAB. The pH simulator was integrated in a granular biofilm model and used to simulate the pH profiles within granules performing the nitritation-anammox process for a range of operating points. The simulation results showed that pH profiles were consistently increasing with increasing depth into the granule, since the proton producing aerobic ammonium oxidizers (AOB) were located close to the granule surface.Despite this pH profile, more NH3 was available for AOB than for anaerobic ammonium oxidizers (AnAOB), located in the center of the granules. However, operating at a higher oxygen loading resulted in steeper changes in pH over the depth of the granule and caused the NH3 concentration profile to increase from the granule surface towards the center. The initial value of the background charge and influent bicarbonate concentration were found to greatly influence the simulation result and should be accurately measured. Since the change in pH over the depth of the biofilm was relatively small, the activity potential of the microbial groups affected by the pH did not change more than 5% over the depth of the granules.

Heat integration is essential for reducing the energy consumption of process industries. However, it may render the dynamic operation more interactive and difficult to control. This paper assesses the implications of heat integration in controllability and performance in energy reduction. The assessment, both on open loop and closed loop, was carried out based on an industrial case study and compared to a modified case without heat integration. Although the heat integrated system displayed a certain deterioration of controllability, the control system made possible an efficient operation. To this goal, different control systems were tested, from a decentralized control system to a model predictive controller. The type of controller used in the process barely influenced the performance of the process since the aim was exclusively to ensure the regulation of the process to fixed setpoints. The reduction of energy consumption achieved thanks to heat integration was considerably larger than the losses due to poor control of the process, confirming the importance of heat integration in energy intensive processes.

With the perspective of investigating a suitable control design for autotrophic nitrogen removal, this work presents a complete model of the SHARON/Anammox reactor sequence. The dynamics of the reactor were explored pointing out the different scales of the rates in the system: slow microbial metabolism against fast chemical reaction and mass transfer. Likewise, the analysis of the dynamics contributed to establish qualitatively the requirements for control of the reactors, both for regulation and for optimal operation. Work in progress on quantitatively analysing different control structure (pairing of controlled variables with manipulated variables) as well as exploring the feasibility of advanced process control including model predictive control.

Design and assessment of control in wastewater systems has to be tackled at all levels, including supervisory and regulatory level. We present here an integrated approach to assessment of control in sewer systems based on modelling and the use of process control tools to assess the controllability of the process. A case study of a subcatchment area in Copenhagen (Denmark) is used to illustrate the combined approach in modelling of the system and control assessment.

In wastewater treatment technologies, employing biofilms or granular biomass, processes might occur at very different spatial and temporal scales. Model development for such systems is typically a tedious, complicated, and time consuming task, which involves selecting appropriate model equations for the different scales, making appropriate and simplifying assumptions, connecting them through a defined linking scheme, analyzing and solving the model equations numerically, and performing parameter estimations if necessary. In this study, a structured framework for modeling such systems is developed. It aims to support the user at the various steps and to reduce the time it takes to generate a model ready for application. An implementation of the framework is illustrated using a simple case study, which considers treatment of a nitrogen-rich wastewater via nitritation.

Energy requirements of distillation processes account for a large percentage of the total energy consumption of the chemical industry. Hence, strategies for heat recovery have been extensively implemented in industrial processes during the last decades. However, operation (including start-up and shut-down strategies) and control of, heat-integrated systems becomes a formidable challenge if not a problem since higher-order systems dynamics occur where different time-scale phenomena interact. Furthermore, if the operation state of the process does not lie close to the designed state, the amount of energy wasted increases, questioning the economics of implementation of heat integration. Modelling is the foremost tool in assessment of chemical process dynamics. In the case of distillation, an extensive literature deals with classic distillation columns whereas much less has been investigated on dynamics of heat integrated distillation systems and the significance of developing a proper plantwide control strategy to ensure optimal and stable operation. This contribution has two aims; firstly to create a systematic framework for the modeler for developing dynamic models of distillation processes with heat-integration. To this end a computer-aided methodology was developed. Secondly, the methodology was tested in an actual industrial case, where a dynamic model for heat-integrated distillation systems is developed and used to analyse and solve operation and control problem. The system studied in the industrial case is composed of two distillation columns in series (operated at 3.5 and 1.5 bar) for recovery of ethanol from a pseudo-binary mixture (heavy key water). Heat recovery is carried out both backward (from the two bottoms flow to the feed) and forward (from the first condensate to the second reboiler). Based on a numerical model of the system, two solutions are presented for improving its control: i) a fine tuning of the existing control strategy, therefore suitable