Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants

An Overview of Software Sensor Applications in Biosystem Monitoring and Control

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems-from cellular interactions to ecological dynamics-are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors

Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.

Rethinking the relationships between gel like structure and sludge dewaterability based on a binary gel like structure model: Implications for the online sensing of dewaterability

The digital transformation of sludge treatment processes requires online sensing of dewaterability. This topic has been attempted for many years based on macroscopic shear rheology. However, the relationship between rheological behavior and dewaterability remains noncommittal, and the reason is unclear. Herein, a binary gel-like structure model was proposed including the interactions network at the supra-flocs level and the gel-like structure at the flocs level. Multiple advanced techniques including optical tweezers were employed to precisely understand the binary gel-like structure and to classify the correlation mechanism between this gel-like structure, rheological behavior, and dewaterability. The analysis of sludge from eight wastewater treatment plants showed the binary gel-like structures at both supra-flocs and flocs levels have significant relationships with sludge dewaterability (p < 0.05). Further deconstruction of the sludge viscoelastic behavior illustrated that the gel-like structure at the supra-flocs level dominates the rheological behavior of sludge. Moreover, the direct description of the binary gel-like structure in four typical sludge treatment processes highlighted the importance of the flocs level's structure in determining the dewaterability. Overall, this study revealed that shear rheology may prefer to stress the interactions network at the supra-flocs level but mask the flocs level's structure, although the latter is important. This observation may provide a general guideline for the design of robust sensors for dewaterability.

Ensuring microbial water quality for on-site water reuse: Importance of online sensors for reliable operation

A growing number of cities and regions are promoting or mandating on-site treatment and reuse of wastewater, which has resulted in the implementation of several thousand on-site water reuse systems on a global scale. However, there is only limited information on the (microbial) water quality from implemented systems. The focus of this study was on two best-in-class on-site water reuse systems in Bengaluru, India, which typically met the local water quality requirements during monthly compliance testing. This study aimed to (i) assess the microbial quality of the reclaimed water at a high temporal resolution (daily or every 15 min), and (ii) explore whether measurements from commercially available sensors can be used to improve the operation of such systems. The monitoring campaign revealed high variations in microbial water quality, even in these best-in-class systems, rendering the water inadequate for the intended reuse applications (toilet flushing and landscape irrigation). These variations were attributed to two key factors: (1) the low frequency of chlorination, and (2) fluctuations of the chlorine demand of the water, in particular of ammonium concentrations. Such fluctuations are likely inherent to on-site systems, which rely on a low level of process control. The monitoring campaign showed that the microbial water quality was most closely related to oxidation-reduction potential (ORP) and free chlorine sensors. Due to its relatively low cost and low need for maintenance, the ORP emerges as a compelling candidate for automating the chlorination to effectively manage variations in chlorine demand and ensure safe water reuse. Overall, this study underscores the necessity of integrating treatment trains, operation, and monitoring for safe on-site water reuse.

Predicting microbial water quality in on-site water reuse systems with online sensors

Widespread implementation of on-site water reuse is hindered by the limited availability of monitoring approaches that ensure microbial quality during operation. In this study, we developed a methodology for monitoring microbial water quality in on-site water reuse systems using inexpensive and commercially available online sensors. An extensive dataset containing sensor and microbial water quality data for six of the most critical types of disruptions in membrane bioreactors with chlorination was collected. We then tested the ability of three typological machine learning algorithms - logistic regression, support-vector machine, and random forest - to predict the microbial water quality as "safe" or "unsafe" for reuse. The main criteria for model optimization was to ensure a low false positive rate (FPR) - the percentage of safe predictions when the actual condition is unsafe - which is essential to protect users health. This resulted in enforcing a fixed FPR ≤ 2%. Maximizing the true positive rate (TPR) - the percentage of safe predictions when the actual condition is safe - was given second priority. Our results show that logistic-regression-based models using only two out of the six sensors (free chlorine and oxidation-reduction potential) achieved the highest TPR. Including sensor slopes as engineered features allowed to reach similar TPRs using only one sensor instead of two. Analysis of the occurrence of false predictions showed that these were mostly early alarms, a characteristic that could be regarded as an asset in alarm management. In conclusion, the simplest algorithm in combination with only one or two sensors performed best at predicting the microbial water quality. This result provides useful insights for water quality modeling or for applications where small datasets are a common challenge and a general advantage might be gained by using simpler models that reduce the risk of overfitting, allow better interpretability, and require less computational power.

Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Hybrid modelling of water resource recovery facilities: status and opportunities

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

Combination of Microscopic Tests of the Activated Sludge and Effluent Quality for More Efficient On-Site Treatment

Container on-site wastewater treatment plants are systems of growing interest in the areas where sewer systems cannot be implemented. In this study, container on-site wastewater treatment plant with low-loaded activated sludge has been examined. The aim of the study was: (i) to assess the efficiency of the plant; and (ii) to evaluate the relationship between the condition of activated sludge and selected parameters of effluent quality. Effluent quality has been characterized by the reliability factor (RF) and technological purity index (TPI). Sludge quality assessment covered measurements of volume (Vo), dry matter (DM), sludge index (SI), and the unit oxygen consumption rate (UOCR). Microscopic analysis has been performed to assess the morphological (flocks) and biotic quality (sludge biotic index, SBI) of activated sludge. The research has been completed by an on-site measurement of dissolved oxygen concentration in an activated sludge chamber with 30 sec. intervals. Results confirmed a significant (p < 0.05) correlation (CC = −0.9277) between biochemical oxygen demand (BOD5) and SBI for the oxygen level in the aeration chamber between 1–2 mg/L. Negative significant correlation (p < 0.05) has also been found between SBI and electrical conductivity (EC) (CC = −0.7478). In the examined case, the optimal EC of the effluent was in the range of 600–800 µS/cm.