Set Of Operating Points Research Articles

CHO stable pool fed-batch process development of SARS-CoV-2 spike protein production: Impact of aeration conditions and feeding strategies.

Technology scale-up and transfer are a fundamental and critical part of process development in biomanufacturing. Important bioreactor hydrodynamic characteristics such as working volume, overhead gas flow rate, volumetric power input (P/V), impeller type, agitation regimen, sparging aeration strategy, sparger type, and kLa must be selected based on key performance indicators (KPI) to ensure a smooth and seamless process scale-up and transfer. Finding suitable operational setpoints and developing an efficient feeding regimen to ensure process efficacy and consistency are instrumental. In this investigation, process development of a cumate inducible Chinese hamster ovary (CHO) stable pool expressing trimeric SARS-CoV-2 spike protein in 1.8 L benchtop stirred-tank bioreactors is detailed. Various dissolved oxygen levels and aeration air caps were studied to determine their impact on cell growth and metabolism, culture longevity, and endpoint product titers. Once hydrodynamic conditions were tuned to an optimal zone, various feeding strategies were explored to increase culture performance. Dynamic feedings such as feeding based on current culture volume, viable cell density (VCD), oxygen uptake rate (OUR), and bio-capacitance signals were tested and compared to standard bolus addition. Increases in integral of viable cell concentration (IVCC) (1.25-fold) and protein yield (2.52-fold), as well as greater culture longevity (extension of 5 days) were observed in dynamic feeding strategies when compared to periodic bolus feeding. Our study emphasizes the benefits of designing feeding strategies around metabolically relevant signals such as OUR and bio-capacitance signals.

Concept design, application and optimization of operational parameters for new forced safety injection tank system

Background The engineered safety systems are designed to execute fundamental safety functions encompassing reactivity confinement, reactivity control and decay heat removal. Failure of any one of these functions can result in severe accident conditions. Passive systems have been implemented as a better option for plant safety. This research proposes a modification of existing safety injection tanks to the new forced safety injection tanks (FSITs) that utilize the backpressure from the pressurizer or steam generator to drive coolant into the reactor core under high pressure conditions. Methods FSITs aim to extend the coping-time during accidents like Station Blackout, providing an extended timing window for deployment of FLEX systems. The mathematical design is proposed and implemented into the thermal-hydraulic input of a nuclear power plant to demonstrate the system’s applicability that was demonstrated by leveraging the conventional PRA approach and risk quantification. Results The suggested system useful when used in the accident scenario of Station black out in-coincident with turbine-driven pumps fail to run. As the proposed design is a conceptual design, the optimization of associated operational parameters and set points is necessary. This optimization is performed using the risk analysis and virtual control environment-based Dynamic Probabilistic Risk Assessment framework. The method and findings of this study affirm that the coping-time for Station Blackout can be significantly extended, ensuring a substantial margin for the effective deployment of the FLEX. Conclusions A concept design of a passive forced safety injection system was suggested and demonstrated by integrating the mathematical model into a thermal-hydraulic model of a nuclear power plant. The parameters were optimized and the results demonstrated that the new system was effective in recovering the nuclear power plant from the accidents such as SBO with turbine-driven pumps fail to operate.

Comprehensive energy demand and usage data for building automation

Buildings are essential in satisfying our daily need for comfort (privacy, protection from weather, etc.) and are responsible for almost half of the world’s total energy consumption. Research at the interface of room comfort and energy efficiency is of critical societal importance. At the same time, there is a lack of publicly available data to optimize important building functions automatically. It is only through data-driven approaches that building automation becomes financially affordable and achieves widespread adoption. In this publication, measurement data from three buildings of the NEST platform are made publicly available. The dataset includes detailed information on energy consumption (electricity, heating, cooling, domestic hot water), building operation (set points, valve openings, windows), and occupant practice (e.g., presence, operation of blinds and kitchen, showering patterns). All data have been measured over four years and with a temporal resolution of 1 minute. This combination of information allows learning the function of different building types (office and residential) and thus addresses important research gaps.

Enhancing Occupant Comfort and Building Sustainability: Lessons from an Internet of Things-Based Study on Centrally Controlled Indoor Shared Spaces in Hot Climatic Conditions.

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases-both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50-100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.

Formal analytics for stealthy attacks against Contingency Analysis in power grids

Contingency Analysis (CA) is a critical part of the Energy Management System of a power grid, ensuring the optimal operating set-points without violating constraints, even in the face of contingency events. CA relies on State Estimation (SE) to perform its analysis, paving the way for Security Constrained Optimal Power Flow (SCOPF) that ensures optimal economic generator dispatches, even in scenarios like n−1 contingencies, such as a single line failure. This integrated approach enhances the grid’s resilience and efficiency under diverse conditions. However, it is shown that an adversary can alter specific power measurements to corrupt the SE without being detected. Such a corrupted estimation will provide CA with a fake model to deal with, which can further lead the grid to an even more vulnerable state. This research formally models and analyzes these attacks on CA, particularly on SCOPF, which are routed from SE. We formally model these complex attacks as a set of constraints and solve them using Satisfiability Modulo Theories (SMT) to synthesize potential attack vectors. Each of these vectors specifies a set of measurements that an adversary can alter to cause one or more transmission lines to be overloaded when some specific contingencies happen. We apply various sensitivity factors to make the solution to this complex synthesis problem tractable. We demonstrate our formal analytics on IEEE bus systems and verify the results using OPAL-RT, a standard virtual power grid testbed. We finally evaluate the tool with respect to various attack and grid characteristics.

Valorization of Food Waste into Single-Cell Protein: An Innovative Technological Strategy for Sustainable Protein Production.

The rapidly increasing population and climate change pose a great threat to our current food systems. Moreover, the high usage of animal-based and plant-based protein has its drawbacks, as these nutritional sources require many hectares of land and water, are affected by seasonal variations, are costly, and contribute to environmental pollution. Single-cell proteins (SCPs) are gaining a lot of research interest due to their remarkable properties, such as their high protein content that is comparable with other protein sources; low requirements for land and water; low carbon footprint; and short production period. This review explores the use of food waste as a sustainable feedstock for the advancement of SCP processes. It discusses SCP studies that exploit food waste as a substrate, alongside the biocatalysts (bacteria, fungi, yeast, and microalgae) that are used. The operational setpoint conditions governing SCP yields and SCP fermentation routes are elucidated as well. This review also demonstrates how the biorefinery concept is implemented in the literature to improve the economic potential of "waste-to-protein" innovations, as this leads to the establishment of multiproduct value chains. A short section that discusses the South African SCP scenario is also included. The technical and economic hurdles facing second-generation SCP processes are also discussed, together with future perspectives. Therefore, SCP technologies could play a crucial role in the acceleration of a "sustainable protein market", and in tackling the global hunger crisis.

Stress-based approach for predicting and improving large-scale HIG mill performance

As a fine and ultrafine grinding technology, the High Intensity Grinding (HIG) Mill is relatively new and there is opportunity to improve its performance by adjusting operational conditions. A laboratory HIG5 mill is commonly used for HIG mill sizing and scale up, however the clear correlation between the operational variables of laboratory mill to their production level is somewhat lacking. This paper presents a stress-based approach for predicting and improving large-scale HIG mill performance. A total of 27 experimental tests were performed with a HIG5 mill over a range of operational variable set points, including media density, media filling level, feed solid content, mill speed and flow rate. The influence of the variables on product size, mill operating power and specific grinding energy was characterized via multivariable linear regression modelling. Based on theoretical stress analysis and empirical calibration with available survey data, the model derived from laboratory testing was calibrated to predict the operation of the commercial HIG1600 mill. An approach was proposed for determining the effect of operational variables using a HIG5 that can be directly applied to reduce energy consumption at a given mill feed rate and product size target for the production scale mill. Results demonstrated that the stress-based approach integrated with laboratory testing can be used for predicting and improving large-scale HIG mill performance.

A machine learning based Bayesian decision support system for efficient navigation of double-ended ferries

Ships can be operated more efficiently by utilizing intelligent decision support integrated with onboard data collection systems. In this study, a Bayesian optimization-based decision support system, which utilizes ship performance models built by machine learning methods, is proposed to help determine the operational set-points of two engines for double-ended ferries. By optimizing the ferries’ power allocation between the stern and bow engines, the Decision Support System (DSS) will simultaneously attempt to keep the ETA of the ferry fixed under a set of operational constraints using the Bayesian optimization. Its objective is to minimize fuel consumption along individual trips. Based on simulation environment, the DSS can reduce at maximum 40 % fuel consumption with no significant change of the ETA. Final full-scale experiments of a double-ended ferry demonstrated an average of 15 %, where at least half of this saving was achieved by the optimized power allocation between bow and stern engines.

A chance‐constrained tube‐based model predictive control for tracking linear systems using data‐driven uncertainty sets

AbstractThis article presents a chance‐constrained tube‐based model predictive control (MPC) method for tracking linear time‐invariant systems based on data‐driven uncertainty sets. By defining the terminal admissible set to consider all the possible steady‐states and reformulating the stochastic tube‐based MPC framework, the proposed method can systematically hedge against the impact of uncertainties and ensure tracking for all reachable operating setpoints. To reduce the conservatism of control performance while enlarging the feasible region, a data‐driven polyhedral uncertainty set is constructed by using the principal component analysis technique, which can effectively capture correlations among uncertain variables. Since state constraint violations in a certain probability are allowed, a probability uncertainty set is constructed by using statistic limit and cutting plane methods to formulate a stochastic tube to ensure constraint satisfaction. The recursive feasibility and stability can be guaranteed if the uncertainties are bounded. The effectiveness of the proposed method is verified by numerical examples and tracking problems of a thickening process.

Intelligent System of Relay Protection of Electrical Network 6-10 kV with the Implementation of Automatic Correction of the Operation Set Point

The purpose of the study is to develop the architecture and working algorithm of intelligent relay protection of an electrical network with an isolated neutral. “Intellectualization” of relay protection is necessary to increase its sensitivity and reliability. This problem is relevant due to the accident rate of overhead power lines and incorrect operation of traditional relays. This is due to the fact that the set point is not adjusted according to the deviation of the line parameters with changing environmental and load conditions. The purpose of the study is achieved by solving the following problems: the concept of supplementing an electronic relay with a mechanism for correcting the operation set point is proposed. The use of the digital shadow of the electrical network is substantiated. The digital shadow interrogates the sensor system. The advantage of using a digital shadow as part of relay protection has been proven. This allows emulating faults and estimating currents to correct the setpoint. The most significant result of the study is an example of the implementation of intelligent relay protection using a digital shadow in the form of an artificial neural network. The application of a neural network for approximating the current function of a two-phase short circuit is demonstrated. The significance of the results lies in the improvement of the sensitivity and selectivity of current protection due to the digital shadow for working out emergency modes. The convenience of operating and reproducing a digital shadow in the form of a neural network is noted.

Novel combinator surface concept for efficiency optimization of ship propulsion system

This paper describes a methodology to design and generate a novel optimal combinator surface suitable for mechanical propulsion systems on ships with controllable-pitch propellers (CPPs). The combinator surface is an extension and enhancement of conventional combinator curves, considering the effect of both the advance speed and the propeller thrust for the selection of optimal operation setpoints for propeller RPM and pitch. The generation of optimal combinator surface does not require the estimation of the ship resistance, wake fraction, and thrust deduction fraction, enabling automatic reference setpoint searching during the steady state as well as acceleration and deceleration. The optimization algorithm is created based on the maximization of the power and propulsion efficiency, taking the prime mover and propeller constraints into consideration. The proposed methodology is illustrated using a longliner as a case study ship. From thrust and ship speed analysis, the proposed combinator surface method benefits vessels working with frequently changing operations. The ship propulsion efficiency is improved greatly for ship acceleration. The comparison of the efficiency performance using the conventional combinator curve and the proposed combinator surface demonstrates that the new concept of combinator surface enables more efficient propulsion for ships operating under varying loading and sea state conditions.

Optimal power flow based coordinated reactive and active power control to mitigate voltage violations in smart inverter enriched distribution network

ABSTRACT Voltage violations are the main problem faced in distribution networks (DN) with a higher penetration of inverter-based generations (IBG). Active and reactive power control from smart inverters (SI) can mitigate such violations. Optimal power flow (OPF)-based control provides more accurate operating set points for the coordinated operation of SIs. Therefore, this paper presents a three-phase OPF-based control on SI-enriched unbalanced distribution networks. To consider this, first three-phase model using the current injection model (CIM) is developed. Later, the optimal active and reactive power set points for SIs are obtained by solving a quasi-dynamic optimization problem. The uniqueness of the proposed method is that it regulates the voltage at the affected nodes by obtaining the optimal set points for the smart inverter. The OPF is implemented with a mathematical CIM in Pyomo and solved using the Knitro solver. The proposed method is compared with the sensitivity-based Volt-Var Control (VVC), Volt-Watt Control (VWC), and combined VVC and VWC methods. The effectiveness of the proposed method is verified in a European low-voltage and CIGRE medium-voltage distribution network with 100% penetration. The analysis shows that the OPF-based control optimizes with less network loss and can maintain voltage violations with less reactive power support.

Dynamic risk-based process design and operational optimization via multi-parametric programming

We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories.

A multi-agent AI reinforcement-based digital multi-solution for optimal operation of a full-scale wastewater treatment plant under various influent conditions

Optimal operating systems have been widely employed to improve the economic and environmental performance of wastewater treatment plants (WWTPs) for efficiently treating discharged water pollutants. However, the multi-optimization approach applied to WWTPs has been inadequate, resulting in a low optimization performance owing to varying influent conditions and inherent complex interactions between manipulated variables. Therefore, this study developed a digital multi-solution for optimal operation of a WWTP based on multi-agent reinforcement learning. Dynamic influent conditions with low, normal, and high influent chemical oxygen demands and total nitrogen composition ratios were generated using a k-means clustering algorithm to accurately reflect operating conditions. A game abstraction method based on a two-stage attention network (G2ANet) algorithm was employed to simultaneously search for three operational setpoints: dissolved oxygen, external sludge recycling, and external carbon dose. The adaptability of the G2ANet-based digital multi-solution for determining setpoints was evaluated by employing newly measured one-year influent data. The results demonstrated the ability of an intelligent G2ANet-based digital multi-solution in identifying optimal setpoints to improve the performance of WWTPs and outperform manual operating systems under varying influent conditions. It reduced aeration energy by 25 % and improved effluent quality by 7 %, while maintaining a similar pumping energy. The G2ANet-based digital multi-solution can innovatively contribute to the smart operation of WWTP operation systems owing to its environmental stability and adaptability.

Modelling and optimal control of growth, energy, and resource dynamics of Hermetia illucens in mass production environment

Mass production of Hermetia illucens insect larvae is now being adopted in many countries and is taking an industrial production approach. Despite abundant literature on factors that affect larvae growth and the optimal static parameters identified in laboratory setup, for an industrial production process it is necessary to identify the trajectories such that the growth as well as the production process is optimal. To achieve this in this work, some of the important requirements and challenges involved thereof are identified and objectives of the automation process are formulated within a model based optimal control setup. Mechanistic models necessary for the optimization framework are derived as differential equations that describe the dynamic variation of resources (feed, water, O2 etc.), energy, and larval biomass. In addition, the elevated metabolic activity of larvae corresponding to the final instar development is identified and also modelled based on the observation from experiments. The mass and energy balance approach used in modelling enables the quantification and distinction of the mass and energy flux components in various levels (e.g. larvae body, growing medium, production environment, and external environment) while holding its applicability for both open and closed/reactor based production setups. Finally, the trajectories generated using the synthesized optimal controller are tested under different scenarios showcasing significant reduction in resource consumption compared to a constant set-point operation of the production setup. Results presented in this work not only showcase the potential of the mechanistic models and their application in identifying the relevant process parameters (e.g. reactor properties such as volume, thermal conductivity, actuator capacities), but most importantly in optimizing the process dynamically and tuning the process objectives as desired (e.g. maximize larvae mass, reduce energy).

Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system

We describe the integration of Bayesian decision theory in a digital twin framework to provide a process optimization tool for a biomass boiler. Our application is the Atikokan Generating Station, a 200 MW, biomass-fired tower boiler operated by Ontario Power Generation in Ontario, Canada. For this analysis, we use all available prior information as well as data from the biomass plant and from science-based models. Our objective is to determine a single-valued operational setpoint for the boiler that satisfies a set of objectives/constraints while accounting for uncertainty in boiler operations and in boiler measurements. This setpoint is then continuously updated at the frequency required by plant operations to provide dynamic control. This process of decision-making under uncertainty is a form of artificial intelligence and provides a formal methodology for making optimized decisions in complex systems. Our methodology consists of defining the decision space where all possible solutions reside, identifying the probability of outcomes given that a specific decision was made, creating a decision/cost model that relates the quantities of interest (QOIs) in the physical system (e.g. gross power output) to the decision QOIs (e.g. dollars), identifying the utility (the value to the user) of each outcome, and maximizing the expected utility (i.e. the decision). Once the decision (operational setpoint) is computed, we can predict all of the QOIs (boiler efficiency, O2 concentration at the outlet, etc.) at the decision point from the science-based model using the parameter distributions computed as part of the Atikokan Digital Twin.

Seawater desalination energy efficiency and emissions reduction at Gold Coast Desalination Plant

This paper investigates the implementation of Artificial Intelligence (AI) and Machine Learning (ML) in the operation of the Gold Coast Desalination Plant (GCDP) to optimise reverse osmosis (RO) membrane efficiency, reduce energy consumption, emissions, and operational costs. The study focuses on using historical and current RO operational data to train and utilise an AI algorithm that generates daily operational setpoints. These setpoints, namely RO Recovery (%) and Permeate Flow (m3/h), directly impact pump power consumption and energy requirements. The collaboration between Veolia and a cleantech start-up involves multiple phases, including system review, performance tests, and iterative optimisation. Three (3) performance tests were conducted at GCDP, revealing unique challenges and findings. Performance Test 1 demonstrated energy savings despite rapidly increasing cartridge filters differential pressure (dP) in the test period. Performance Test 2 encountered underperformance of an energy recovery device (ERD), resulting in increased feed pressure and with it, power consumption. Performance Test 3 highlighted the impact of fluctuating feedwater temperature on energy consumption. Overall, the AI model showcased its ability to recommend setpoints within operational limits and achieve required permeate flow with reduced energy, even under unfavourable conditions. The initiative at GCDP resulted in an estimated 1.1% energy reduction, corresponding to approximately 0.023 kWh/m3 and the potential to save up to 1044 MWh of energy and 762 tonnes of CO2-e per annum. The study suggests further exploring AI and ML applications to optimise treatment plant operations, including varying permeate flow setpoints and improving data transfer and setpoint implementation methods. These advancements hold promise for enhanced energy efficiency and cost reduction in desalination plants worldwide.

Sensor setpoints that ensure compliance with microbial water quality targets for membrane bioreactor and chlorination treatment in on-site water reuse systems

Widespread implementation of on-site water reuse systems is hindered by the limited ability to ensure the level of treatment and protection of human health during operation. In this study, we tested the ability of five commercially available online sensors (free chlorine (FC), oxidation-reduction potential (ORP), pH, turbidity, UV absorbance at 254 nm) to predict the microbial water quality in membrane bioreactors followed by chlorination using logistic regression-based and mechanism-based models. The microbial water quality was assessed in terms of removal of enteric bacteria from the wastewater, removal of enteric viruses, and regrowth of bacteria in the treated water. We found that FC and ORP alone could predict the microbial water quality well, with ORP-based models generally performing better. We further observed that prediction accuracy did not increase when data from multiple sensors were integrated. We propose a methodology to link online sensor measurements to risk-based water quality targets, providing operation setpoints protective of human health for specific combinations of wastewaters and reuse applications. For instance, we recommend a minimum ORP of 705 mV to ensure a virus log-removal of 5, and an ORP of 765 mV for a log-removal of 6. These setpoints were selected to ensure that the percentage of events where the water is predicted to meet the quality target but it does not remains below 5%. Such a systematic approach to set sensor setpoints could be used in the development of water reuse guidelines and regulations that aim to cover a range of reuse applications with differential risks to human health.

Power minimization of gas transmission network in fully transient state using metaheuristic methods

Abstract In gas transmission networks, the pressure drop caused by friction is one of the main operation costs that is compensated through consuming energy in the compressors. In the competitive market of energy, considering the demand variation is inevitable. Hence, the power minimization should be carried out in transient state. Since the minimization problem is severely nonlinear and nonconvex subjected to nonlinear constraints, utilizing a powerful minimization tool with a straightforward procedure is very helpful. In this paper, a novel approach is proposed based on metaheuristic algorithms for power minimization of a gas transmission network in fully transient conditions. The metaheuristic algorithms, unlike the gradient dependent method, can solve the complicated minimization problem without simplification. In the proposed strategy, the cost function is not expressed explicitly as a function of minimization variables; therefore, the transient minimization can be as precise as possible. The minimization is carried out by a straightforward methodology in each time sample, which leads to more precise solutions as compared to the quasi transient minimization. The metaheuristic minimizer, called the particle swarm optimization gravitational search algorithm (PSOGSA), is utilized to find the optimum operating set points. The numerical results also confirm the accuracy and well efficiency of the proposed method.

Enhancing resource recovery via cranberry syrup waste at the Wisconsin Rapids WRRF: An experimental and modeling study

The Wisconsin Rapids Wastewater Treatment Plant (WRWWTP) is faced with a more stringent effluent phosphorus requirement that will drive capital investment between 2020 and 2025. The facility will need to achieve a monthly average value of 0.36 mg L−1 of total phosphorus (TP). While the facility has sufficient influent carbon to drive a conventional enhanced biological phosphorus removal (EBPR) configuration, the existing infrastructure makes the addition of influent selector zones cost prohibitive. Underutilized aeration basin capacity was repurposed for testing return activated sludge (RAS) fermentation. The WRWWTP began pilot testing of RAS fermentation in April 2021. The facility moved through a series of operational setpoints to optimize phosphorus removal in a sidestream RAS (SSR) configuration, including RAS diversion, decrease of DO in aeration basins and chemical dosing shutoff. One of the key implementations was the addition of cranberry syrup waste to provide additional carbon for RAS fermentation, converting the process to a SSR plus carbon (SSRC) configuration. By the end of the testing period, effluent total phosphorus was averaging less than 0.4 mg L−1 with no chemical addition. A model was developed in the SUMO platform and was used to capture orthophosphate trends during the testing period. The model investigated microbial population dynamics and found that the operational changes including RAS diversion, chemical dosing shutoff and cranberry syrup waste addition impacted the enrichment of phosphorus accumulating organisms (PAO). After performing a sensitivity analysis on hydrolysis parameters, the predicted hydrolysis rate around 1.8–1.9 mg COD g VSS−1 hr−1 was found to match the batch rate testing data. This is the first study where cranberry syrup waste was used to successfully enhance EBPR performance, resulting in 90% TP removal. While further research is needed regarding the composition of the waste matrix and the microbial community composition, this expands the routes for resource recovery in the field of wastewater treatment.