Autonomous monitoring system for quantification of heavy metal ions in water

Abstract

The present application describes an autonomous monitoring system for the in situ electrochemical detection of heavy metal ions like Zn and Ni in water. The monitoring system is able to collect and process water samples, perform stripping voltammetry analysis and estimate levels of heavy metals in an automated way. The developed system integrates a sample processing module together with modified screen-printed carbon sensors to allow the selective and sensitive detection and quantification of metal ions. The system can perform analytical readings of the sensors and features a data analysis algorithm for the estimation of the metal ions based on the analysis performed. The monitoring system also supports a range of connectivity options and operation modes, for easy and flexible implementation on different water monitoring applications.

Description

Autonomous monitoring system for quantification of heavy metal ions in water

[0001] The present application describes an autonomous monitoring system for the in situ electrochemical detection of heavy metal ions like Zn(II) and Ni(II) in water effluents.

[0002] Water pollution by heavy metal ions (HMI) contamination poses a serious problem. Anthropogenic activities such as metal-based industries are a major source of HMI contamination in surface water and groundwater, as it exploits the release of heavy metals naturally present in the Earth’s crust resources into the environment, increasing the concentration of these elements to dangerous levels toxic to life[NPL1],[NPL2].

[0003] HMI may enter the ecosystem through the hydrosphere, wastewater treatment of industrial effluents before disposal into the environment or municipal streams can reduce the risks associated to HMI environmental pollution[NPL3]~[NPL5]. However, wastewater treatment is a challenging task and HMI removal processes require constant monitoring, which rely on expensive and time-consuming analytical techniques for the quantification of heavy metal ions, such as Atomic Absorption Spectroscopy (AAS), Atomic Emission Spectroscopy (AES), and Inductively Coupled Plasma (ICP) coupled with optical emission spectroscopy (OES) or mass spectrometry (MS)[NPL6]. These techniques require qualified users and allow only for ex-situ analysis cause the non-portability of the instrumentation. Thus, the time lag between sample collection and the final analytical results offers only short timespan characterization of the wastewater treatment plant (WWTP) effluent, which may not be sufficient considering the changes that can happen in the effluent composition, compromising the compliance of the effluent with the local regulations and the appropriate operation of the WWTP processes. The frequent monitoring of WWTP effluent could offer a higher resolution for characterizing the wastewater over time, facilitating the control and optimization of the WWTP processes according to the wastewater composition.

[0004] As an alternative, analytical techniques based on electrochemical sensors represent an affordable and easy to use solution, able to guarantee sensitive results with less and small equipment in short turnaround times. Studies using electrochemical sensors have shown promising solutions to detect and quantify HMI in natural and waste waters. Over the last years several efforts developing monitoring devices have been made integrating electrochemical sensors in automatic systems for monitoring of HMI in natural water, costal water and wastewater[NPL7]~[NPL11]. More recently, screen printed carbon electrodes (SPCE) offer a cheap and a chemical stable substrate with wide potential window for on-site analysis of HMI in environmental applications, the small footprint and requirements for simple, affordable and miniaturized systems offer significant advantages for on field analysis as compared to the conventional analytical chemistry instruments[NPL19],[NPL20]. The integration of SPCE sensors in autonomous monitoring systems have been reported in literature highlighting the suitability of such integrated systems for the detection of HMI[NPL14]. Leveraging this technology alongside the capabilities of internet of things (IoT) networks, decentralized smart water quality monitoring systems offer near-real-time communication providing preventive mechanisms to support decision making[NPL15]. A new generation of smart WWTP systems is emerging, prepared to enhance efficiency and efficacy in water quality management.

[0005] Document CN107796811A, entitled Multi-parameter water quality monitor, discloses a multi-parameter water quality monitor, comprising a box body (1); the box body is internally provided with a raw liquid pump (29) and a second ant tube (21); the raw liquid pump (29) is connected with a colorimetric tube (115) with two inlets and an outlet sequentially by means of a first ant tube (9) and a second silica gel tube (13); a main control panel (18) is installed at one side of the second ant tube, and a valve control panel (19) and a pump control panel (20) are installed at the other side of the second ant tube. The multi-parameter water quality monitor can be used for detecting the parameters of multiple heavy metal ion components at a time, is simple and clear to operate, and prevents personnel from being directly hurt by liquid and chemical reagents during detection; the device adopts intelligent processing, and results can be selected to be looked over on a large capacitive display screen after detection is finished.

[0006] Document CN110907620A, entitled Method for monitoring ions in water of sewage treatment plant, discloses a method for monitoring ions in water of a sewage treatment plant. The method comprises the following steps of 1, conveying a collected sewage sample into a detection module; 2, measuring the concentration value of each metal ion and element ion through each sensor; 3, using an analysis module to generate an analysis result according to the concentration of each ion in the sewage, and displaying the analysis result through a display module. According to the present invention, the concentration of each metal ion and element ion in the sewage is measured through a microprocessor, and the data analysis result is generated through the analysis module, and finally, the analysis result is displayed in real time through the display module, so that the detection result of the ions in the sewage is more detailed. Through the arrangement of a first buffer clamping pad, a second buffer clamping pad and a fixed bottom block, a collector can be effectively fixed and clamped under the condition of not damaging the collector, the collector can be conveniently fixed and separated through the fixed bottom block, and the collector can be conveniently stored and cleaned.

[0007] The present invention describes an automated system for heavy metal ions monitoring in water comprising a sample processing module; an analysis module; and a central control module; characterized by the central control module being configured to monitor and quantify heavy metal ions in water by means of sample collection and processing, electrochemical analysis and data processing.

[0008] In a proposed embodiment of present invention, the sample processing module comprises a sample collection and filtration system; a sample dilution and mixing system; a cleaning system; and a control board configured to control and automate any of the previous systems.

[0009] In another embodiment, the control board comprises a microcontroller unit configured to integrate fluidic actuators as the range of first valves and / or a range of second valves and / or a main pump and / or a stepper motor pump and / or a Zn recirculation brushed motor pump and / or a Ni recirculation brushed motor pump.

[0010] In another embodiment, the automated system comprises an IoT interface configured to enable an installation and / or integration with local or remote data networks.

[0011] In another embodiment, the analysis module is configured to perform electrochemical analysis over a set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water.

[0012] In another embodiment, the set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water comprising at least one Zn sensor and at least one Ni sensor is configured to enable the evaluation of sample dilution conditions.

[0013] In another embodiment, the set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water comprising at least one Zn sensor and at least one Ni sensor is configured to enable the estimation of metal ions concentration.

[0014] In another embodiment, the invention further comprises an analytical platform characterized by integrating at least one potentiostat configured to perform readings from each of the Zn sensors through a Zn cluster.

[0015] In another embodiment, the at least one potentiostat of the analytical platform is configured to perform readings from each of the Ni sensors through a Ni cluster by means of multiplexing routing connectors.

[0016] In another embodiment, the central control module is configured to execute a data analysis peak search algorithm for the estimation of the metal ions, Zn and Ni, concentration by means of the recognition of a peak curvature on the current / potential curve.

[0017] In another embodiment, the central control module is configured to manage and control the sample processing module and / or the analysis module.

[0018] The present invention also discloses the operation method of the automated system for water monitoring previously described and which is characterized by comprising the steps of collect water samples through a range of first valves which are connected to a main pump configured to collect water samples from a water inlet; filter the water samples through a set of filters with different porosities; mixing the filtered water samples with an electrolyte solution, transferring filtered samples to the Zn mixing reservoir or Ni Mixing Reservoir; recirculating the resulting solution in the mixing reservoir, ensuring a proper mixing of the electrolyte buffer solution with the aliquot sample; measurement of the processed sample through an analysis module; analysis of the acquired data and selection of the appropriate dilution factor through the central control module; measurement of the processed sample through an analysis module; estimation of the concentration values of metal ions Zn and Ni through the central control module using the data analysis algorithm; transmission of the calculated concentration through the communication interface; cleaning of the system through a cleaning system.

[0019] In a preferred embodiment, the operating method is characterized by comprising an electrochemical detection method based on a two-stage measurement including an accumulation step and a cathodic stripping differential pulsed voltammetry scan for the determination of Ni.

[0020] In a preferred embodiment, the operating method is characterized by comprising an electrochemical detection method based on two-stage measurement including an accumulation step and an anodic stripping differential pulsed voltammetry scan for the determination of Zn.

[0021] In an additional embodiment of present invention, the sample collection and filtration system may comprise a main pump, configured to collect water samples from a water inlet; a range of first valves, connected to the main pump; a filtration system, composed by a set of filters with different porosities, connected to the range of first valves; a set of independent reservoirs, connected to the filtration system; a range of second valves, connected to the set of independent reservoirs, and which enable a filtrated water output sample; a range of water flow controllers, serially connected with the range of first valves and the range of second valves; and adequate tubing for the distribution of collected water samples between the previously identified elements.

[0022] Yet in an additional embodiment of present invention, the sample processing module may further comprise a first stepper motor pump configured to enable the collection of an aliquot sample from the filtrated water output sample into a Zn mixing reservoir to be mixed with an electrolyte buffer solution comprised in an electrolyte buffer solution reservoir pumped into the Zn mixing reservoir by second stepper motor pump, the sample processing module further comprising a Zn recirculation brushed motor pump configured to recirculate a liquid in the Zn mixing reservoir, ensuring a proper mixing of the electrolyte buffer solution with the aliquot sample.

[0023] Yet in an additional embodiment of present invention, the first stepper motor pump may ensure the pumping of the water output samples, through the Zn sample valve and a Ni sample valve, into the Zn mixing reservoir and a Ni Mixing Reservoir; the electrolyte buffer solution comprised in the electrolyte buffer solution reservoir is enabled into both of Zn mixing reservoir and Ni mixing reservoir through the Zn buffer valve and the Ni buffer valve respectively; the Zn mixing reservoir comprises an Zn recirculation brushed motor pump to enable the recirculation of the Zn liquid in the Zn mixing reservoir, and the Ni mixing reservoir comprises an Ni recirculation brushed motor pump to enable the recirculation of the Ni liquid in the Ni mixing reservoir.

[0024] Yet in an additional embodiment of present invention, the sample processing module may further comprise a cleaning system composed by two cleaning solutions, water and nitric acid, stored in independent reservoirs, an acid reservoir and a water reservoir.

[0025] Yet in an additional embodiment of present invention, the Zn cluster may comprise a Zn Sensor Array 1, a Zn Sensor Array 2, a Zn Sensor Array 3, and a Zn Sensor Array 4 comprising five Zn sensors each, and the Ni cluster may comprise a Ni Sensor Array 1, a Ni Sensor Array 2, a Ni Sensor Array 3 and a Ni Sensor Array 4 comprising five Ni sensors each.

[0026] Yet in an additional embodiment of present invention, each of the Zn sensors and each of the Ni sensors may comprise a modified screen-printed carbon electrode featuring a carbon working electrode (WE) and counter electrode (CE) and a silver / silver chloride reference electrode (RE) to allow the selective and sensitive detection and quantification of metal ions.

[0027] Yet in an additional embodiment of present invention, the surface of the carbon working electrode (WE) may be chemically modified by means of dimethylglyoxime for the selective accumulation of Ni and / or by means of electrodeposition of Bismuth for the selective accumulation of Zn.General Description

[0028] The present application describes an autonomous monitoring system for the in situ electrochemical detection of heavy metal ions like Zn and Ni in water.

[0029] The herein disclosed solution is meant for water control, monitoring and treatment, metal recovery and metalworking companies which use heavy metals in specific productive processes and need to monitor Zn and Ni in the (liquid) effluent of the process.

[0030] It is particularly relevant to monitor and control the quantity of the metals in an industrial process effluent and respective wastewater to control the process, improve the water treatment and / or guarantee the appropriate discharge of the effluents.

[0031] Conventional methods for the determination of trace levels of heavy metals are based on laboratory analytical spectrometric techniques, like Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) or Inductively Coupled Plasma Mass Spectroscopy (ICP-MS), Atomic Absorption Spectroscopy (AAS) and Atomic Emission Spectroscopy (AES), that despite being highly selective and sensitive, are largely time and cost consuming, requiring qualified users, and only allowing ex-situ analysis cause of non-portability of the instrumentation and the necessity of complex sample processing.

[0032] The proposed technology is an automated monitoring system (10) for the quantification of metals ions, Zn and Ni, in water effluents. The monitoring system is composed by Sample Processing Module (SPM - 20), Analysis Module (AM - 30) and Central Control Module (CCM - 40). The different modules allow to collect, filter, dilute and mix the sample, as well as clean the circuit (SPM); perform electrochemical analysis (AM); data processing and management of all operations (CCM). Moreover, the system enables flexible operation modes such as connectivity capabilities (for installation in external networks) as well as stand-alone operation (local data storage).

[0033] The Sample Processing Module (SPM - 20) is a fluidic system composed by a collection and filtration system (set of filters, dilution and mixing system, used to dilute and mix the sample with the electrolyte buffer solution, and a cleaning system. The module include also pumps, valves, flow controllers and adequate tubing for the distribution of fluids within the monitoring system. A custom PCB integrates a microcontroller unit and several fluidic actuators (pumps and valves) part of the SPM. A firmware oversees the low-level routines for controlling the operation of the actuators and the communications with the CCM via a serial protocol.

[0034] The Analysis Module (AM - 30) is composed by modified sensitive sensors within microfluidic chambers and embedded Original Equipment Manufacturer (OEM) potentiostat modules which allows the quantification of the metal ions.

[0035] Data analysis is performed by a custom developed Python algorithm that detects and characterizes the current spectrum acquired by the OEM module and estimates the concentration of the target metal ion. The algorithm application is based on the analysis of curvature changes on the voltammogram to identify low and high concavity regions, which are used to identify current peaks. After the detection of the peak, the software calculates the height of the peak which is proportional to the level of the metal concentration.

[0036] The Central Control Module (CCM - 40) manages and enables the autonomous operation of the monitoring system. A single-board computer (SBC) is used as primary control unit due to its cost-effectiveness and adaptability. A tailored python program allows to configure and manage all the necessary communication protocols between the modules part of the system. The SBC runs the algorithm for data analysis and processing to calculate the contamination levels of the processed sample. The control system has an interface for communication with outer systems over Ethernet based protocols, such as Ethernet / IP, enabling possible integration in IoT networks. The control system can be configured to operate in other modes, stand-alone mode or remote mode. In the last mode the device is connected to a cloud database allowing for remote data storage and remote accessibility.

[0037] The autonomous monitoring system for the detection of heavy metal ions in water presents a reference framework for electrochemical-based analysis, featured with flexible functions and modules, which can be configured to detect a diverse range of chemical elements like lead, iron, mercury, cadmium, zinc, arsenic, copper, chromium, and other heavy metals that are detected using electrochemical approaches.

[0038] These elements are important in various industrial, technological, and biological contexts, but many of them (like lead, mercury, cadmium, and arsenic) are also toxic and pose significant environmental and health risks if not properly managed.

[0039] Developing a monitoring system for these chemical elements may attract significant interest across various industrial sectors since they have substantial applications and implications in numerous industries due to their chemical properties, toxicity, and environmental impact, just to refer a few: Mining and Metallurgy, Chemical Manufacturing, Electronics and Electrical Equipment Manufacturing, Automotive Industry, Energy Production, Water Treatment and Supply, Food and Beverage Industry, Pharmaceuticals and Healthcare, Agriculture, Construction Industry, Waste Management and Recycling, Textile and Leather Processing, Environmental Agencies and Regulatory Bodies, Aerospace and Defense, Academic and Research Institutions.

[0040] The developed autonomous electroanalytical-based monitoring system is portable and easy to use, guaranteeing sensitive results with smaller footprint equipment in short turnaround times and able to operate at room temperature.

[0041] The main features include: Portability (allow in situ analysis); Use of low power and low-cost instrumentation; Fully automated operational system (no personnel required); Fully automatic sample processing; No acidification needed: sample processing based on filtration and dilution of sample with an electrolyte-buffer solution; Automatic adjustment of the dilution factor for accurate results; Fast analysis, within minutes, by the use of a fast analysis technique – stripping voltammetry; No need to use internal standard or standard addition methods, decreasing the time of analysis and complexity of the sample processing (external calibration is carried out); Automatic data analysis and processing (custom algorithm).

[0042] The developed monitoring system allows the sensitive and selective detection and quantification of metal ions Zn and Ni, fully autonomously, which is a major advantage against the existing methods available, ICP-OES / MS and AAS / AES as well as to the commercial spectrophotometric kits, used for the detection of metals, since the analysis is performedin situ, do not require any personnel to collect and process the sample, neither to run the analysis, taking no longer than 25 min to acquaint the result of the concentration levels. The monitoring system includes lower cost and smaller footprint instrumentation than the standard analytical methods used for HMI quantification and was engineered for low maintenance, ensuring consistent performance and reliability. The system provides promptly precise and reliable results for accurate metal estimation.

[0043] The system supports different operation and connectivity modes, allowing exploitation in multiple dimensions.

[0044] For better understanding of the present application, figures representing preferred embodiments are herein attached which, however, are not intended to limit the technique disclosed herein.

[0045] overall illustration of the components of the monitoring system (10).

[0046] illustration of the sample collection and filtration systems scheme.

[0047] illustration of the dilution and mixing system scheme.

[0048] illustration of the sample processing module including all the primary systems: sample collection and filtration system, dilution and mixing system, and cleaning system.

[0049] - illustration of the analysis module. Integration of the OEM potentiostats with the connectors for the sensor’s.

[0050] - illustration of the modified SPCE with dimethylglyoxime (DMG): Optical microscope image (left) and SEM image (right) In inset a mapping showing the chemical composition (C, N, O) analysis of the film acquired by EDS.

[0051] - illustration of DPV scans obtained by the measurement of varying concentration of Ni(II) in ammonium buffer between 5 and 100 µg L-1 (left) and corresponding calibration curve (right).

[0052] – illustration of the results obtained for several effluent samples from entrance and exit of the WWTP using different methods for Ni(II) detection.

[0053] - illustration of SEM images of bismuth modified SPCE surface using different PED deposition conditions, from small particles (top left) to bigger particles formation (top right), to film formation (bottom).

[0054] – illustration of DPV scans obtained by the measurement of varying concentration of Zn(II) in ammonium buffer between 50 and 1000 µg L-1 (left) and corresponding calibration curve (right).

[0055] – illustration of the results of the analysis of several samples of effluents from the entrance and exit of the WWTP using different analysis techniques for Zn d chambers.

[0056] – illustration of automated peak search analysis: Curvature analysis (A); Peak height detection and calculation (B).

[0057] – illustration of the data analysis performance validation. Comparison with PSTrace peak search algorithm.

[0058] – illustration of the monitoring system scheme.

[0059] With reference to the figures, some embodiments are now described in more detail, which are however not intended to limit the scope of the present application.

[0060] The monitoring system (10) is composed by different modules enabling different functionalities such as: sample processing, analytical measurements, data handling and transmission.

[0061] This section provides a detailed description of the monitoring device modules and communication protocols implemented to connect the various parts of the device. The monitoring system (10) is divided in three modules, interconnected through adequate communication systems, which comprise: a sample processing module (20); an analysis module (30), and a central control module (40).

[0062] Additionally, an IoT interface was implemented, allowing the system (10) to be installed, and / or integrated with, in external networks. An overall scheme of the monitoring system (10) modules and IoT connectivity is presented in. Each module and the IoT connectivity, will be explained in more detail further in this document.

[0063] The internal network of the monitoring system (10) adopts a star topology architecture. At the heart of this network is the central control module (40), which serves as the central connection point. Each individual module within the system (10) communicates directly with the central control module (40) using a specific protocol (detailed over the next sections), facilitating efficient data exchange and centralized control.

[0064] SAMPLE PROCESSING MODULE

[0065] The sample processing module (20) provides a solution to handle the liquid samples, reagents and essential processes within the device to prepare the sample for the analysis. Its primary configuration is composed of: a sample collection and filtration system (); a sample dilution and mixing system () and a cleaning system ().

[0066] The sample collection and filtration system, as illustrated in, comprises at least: a main pump (201), configured to collect water samples from a water inlet (200); a range of first valves (202, 203, 204, 205), connected to the main pump (201); a filtration system, composed by a set of filters (206, 207, 208, 209) with different porosities; a set of reservoirs for storage of filtered sample (210, 211, 212, 213); a range of second valves (214, 215, 216, 217), connected to a stepper motor pump (219); adequate tubing for the distribution of collected water fluids.

[0067] The main pump (201) of the sample collection and filtration system, as illustrated in, comprises a compact peristaltic pump, in a preferred embodiment providing a rate of 60 mL min-1, and which is used to collect a water sample, through a water inlet (200), and force it through the filtration system composed of the set of filters (206, 207, 208, 209) with different porosities, for example, 1 µm, 0.45 µm and / or 0.22 µm. The filtrated samples from each independent set of filters (206, 207, 208, 209) are then stored in a set of independent reservoirs (210, 211, 212, 213), preferably with 15 mL capacity. The filter sets (206, 207, 208, 209) and independent reservoirs (210, 211, 212, 213) comprised in the developed sample processing module (20) are used on each sample collection avoiding cross contamination and filter blockage. The collection path is selected by small footprint normally closed valves, using the range of first valves (202, 203, 204, 205) and the range of second valves, (214, 215, 216, 217). Adequate tubing, ranging for example from 1 / 8’’ to 1 / 32’’, connects the different embodiments of the sample collection and processing system. The sampling and filtration path, as well as the sample reservoir that stores the sample after the filtration, is enabled by the range of first valves (202, 203, 204 and 205). In a proposed embodiment, four paths are ensured and managed by the range of first valves (202, 203, 204 and 205) and the range of second valves (214, 215, 216, 217), the last providing a water output sample (218).

[0068] As illustrated in the proposed embodiment of, a first path between the main pump (201) and the output (218) comprises a valve V1A (202) connected to the pump (201) and to the first filter set F1A (206), said filter (206) is then connected to the first reservoir (210), said reservoir (210) is then connected to the valve V1B (214), which provides a portion of the water output sample (218). Additionally, a second path between the main pump (201) and the output (218) comprises a valve V2A (203) connected to the pump (201) and to the second filter set F2A (207), said filter (207) is then connected to the second reservoir (211), said reservoir (211) is then connected to the valve V2B (215), which provides a portion of the water output sample (218). Additionally, a third path between the main pump (201) and the output (218) comprises a valve V3A (204) connected to the pump (201) and to the third filter set F3A (208), said filter (208) is then connected to the third reservoir (212), said reservoir (212) is then connected to the valve V3B (216), which provides a portion of the water output sample (218). Additionally, a fourth path between the main pump (201) and the output (218) comprises a valve V4A (205) connected to the pump (201) and to the fourth filter set F4A (209), said filter (209) is then connected to the fourth reservoir (213), said reservoir (213) is then connected to the valve V4B (217), which provides a portion of the water output sample (218).

[0069] The sample dilution and mixing system, illustrated in, comprises at least: a precise stepper motor pump (219) to ensure the intake of the filtered water samples (218); the Zn mixing reservoir (227) and the Ni mixing reservoir (230); an electrolyte buffer solution reservoir (222); a recirculation brushed motor pump for Zn mixing (228) and one for Ni mixing (231); a couple of sample valves (221,233) connected to the mixing reservoirs (227, 230); a couple of electrolyte buffer valves (225,226) also connected to the mixing reservoirs (227,230).

[0070] The sample dilution and mixing system of, wherein after the filtration, the mixing of water output sample (218) is achieved by a smaller and precise stepper motor pump (219) used to collect an aliquot of sample (220) from the filtrated sample reservoir at a flowrate of 1000 μL min-1and mixing with an electrolyte solution, transferring filtered samples to the Zn mixing reservoir (227) or Ni Mixing Reservoir (230). The aliquot sample (220) is sent to a mixing reservoir (227), with preferred 50 mL capacity, to be mixed with an electrolyte buffer solution (223) comprised in an electrolyte buffer solution reservoir (222), preferably with 0.1 M Ammonium buffer solution with pH 9.24, which is pumped into the mixing reservoir (227) by another stepper motor pump (224). In a preferred embodiment of the invention, a Zn recirculation brushed motor pump (228) is used to recirculate the liquid (229) in the mixing reservoir (227), ensuring a proper mixing of the electrolyte buffer solution (223) with the aliquot sample (220). To handle the sample and reagents paths, two additional memory shape alloy valves, a Zn buffer valve (225) and a Zn sample valve (221) are used as illustrated in. This proposed embodiment, as suggested by the nomenclature of the valves, is for determining the Zn(II). Finally, the overall dilution and mixing system is composed of two similar mixing systems, one for Ni(II) and other for Zn(II), as the one suggested in. In this proposed embodiment, one more mixing tank (230) is added, and additional valves for the Buffer (226) and for selecting the sample path (233), and one more recirculation pump (231). The stepper motor pump (219) ensures the pumping of the water output samples (218), through the Ni sample valve (233), into the independent mixing reservoirs, Zn mixing reservoir (227) and Ni Mixing Reservoir (230). The electrolyte buffer solution (223) comprised in an electrolyte buffer solution reservoir (222) is enabled into both of said mixing reservoirs through the Zn buffer valve (225) and the Ni buffer valve (226) respectively. In each of the mixing reservoirs, a recirculation brushed motor pump ensures the liquid mixture, i.e., the Zn mixing reservoir (227) comprises an Zn recirculation brushed motor pump (228) to enable the recirculation of the filtrated sample for Zi detection (229) in the Zn mixing reservoir (227), and the Ni mixing reservoir (230) comprises an Ni recirculation brushed motor pump (231) to enable the recirculation of the filtrated sample for Ni detection (232) in the Ni mixing reservoir (230).

[0071] Apart from the previous systems described in the previous sections, the sample processing module (20), as illustrated in, also comprises a cleaning system to enable the cleaning of the mentioned tubing through which the water samples conducted and also the previously cited mixing reservoirs after each sampling analysis. This proposed cleaning system is composed by two cleaning solutions, water (240) and acidic media (235), such as nitric acid 5%, stored in high density polyethylene reservoirs, or other adequate materials like Teflon (for example 1L), an acidic media Reservoir (234) and a water reservoir (239). The tubing and reservoirs are flushed with both solutions, first with acid and then with water, to clean fluidic system prior the next analysis. This helps reducing the risks associated to cross contamination. The water (240) and acidic media (235), as well as an air input (236) are, enabled in the tubing circuit, particularly within the water output samples (218) circuit, like illustrated in, through a water valve (241), an acidic media valve (238) and an air valve (237). Additionally, both the water (240) and acid 5% (235) are also injected in the tubing, prior to the range of first valves (202, 203, 204 and 205), through an additional set of valves, an input air valve (242) and an acid input valve (243). The overall sample processing module (20) arrangement, with all of the prior references and proposed embodiments, is disclosed in. The proposed arrangement disclosed in this figure is based on the previous described embodiments of Figures 2 and 3, and merges the sample collection system, the filtration system, the cleaning system and the mixing module. The cleaning system is positioned between the output of the filtration system and the input of the mixing module.

[0072] To automate the operations of the sample processing module (20), a control board, comprising a microcontroller unit (MCU), was developed integrating all the necessary electric actuators for handling the liquids in the tubing. In one of the preferred embodiments, the developed PCB integrates two port expander units for actuating on the sixteen valves comprised in the sample processing module (20), one 8-bit port expander and a 16-bit port expander. The port expander was connected to the MCU as I2C slaves. To control the stepper motor pumps (219, 224), two stepper driver modules were integrated in the PCB, the connection is established directly with the MCU General-Purpose Input / Output (GPIO) and the rotation speed of the pumps was controlled via pulse width modulation (PWM), where each pulse generated one step rotation in the pump. For the three additional Brushed Motor pumps (201, 228, 231), three brushed motor H-bridge drivers were implemented in the PCB, where the control was also achieved by direct connection to the MCU GPIO, PWM was used to control the speed of the pumps. Additionally, to manage the different power requirements of the actuators, a power regulator module (DC-DC) was integrated in the PCB. The 12 V input power feeds the heavy-duty BM pump (201) and the DC-DC converter which output a voltage of 5 V used to power up the MCU and the drivers, by its turn the MCU board convert’s the power to 3.3 V which are used to power the remaining valves and pumps.

[0073] The sample processing module (20) controlling application was developed using a bare-metal approach. The developed firmware integrates several routines and function to handle each of the actuators and a serial protocol via UART. The routines implemented in the sample processing module (20) firmware allow: Stepper drivers controlling; Brush drivers controlling; Valves controlling (On, off); I2C slave handling; Serial communication protocol via UART.

[0074] ANALYSIS MODULE

[0075] The analytical capabilities of the overall monitoring system (10) are enabled by the analysis module (30). This module allows the system to perform electrochemical analysis over a set of sensors developed for the analysis of heavy metal ions levels in water. This module primary function is to enable the device comprehensive analysis. A total of twenty sensors per heavy metal are integrated in this analysis module (30) and connected to the analytical platform (300) through a pair of multiplexing routing connectors (301, 302) that allows the system (10) to select which sensor is being used for the analysis. The sensors comprised in the module (30) are divided in two clusters a Zn cluster and a Ni cluster, respectively connected to two electrical connectors, the Zn cluster electrical connector and the Ni cluster electrical connector. Twenty sensors are allocated for the Ni(II) analysis and grouped in sets of five electrodes designated as Ni Sensors Arrays 1, 2, 3 and 4 (3021, 3022, 3023 and 33024), and twenty sensors are allocated for Zn(II) analysis and grouped in sets of 5 electrodes designated as Zn Sensor Arrays 1, 2, 3 and 4 (3011, 3012, 3013 and 3014). Each set of sensors is used to perform a single analysis. The sensors installation architecture is illustrated in.

[0076] A detailed description of the analytical module (30) is provided in the next sections.

[0077] SENSORS

[0078] The analysis module (30) is composed by modified screen-printed carbon electrodes (SPCEs) integrated into a microfluidic Polymethyl Methacrylate (PMMA) chamber. As previously mentioned, there are five sensors in each chamber / array, arranged in two groups, two of the sensors enable to evaluate the sample dilution conditions and the remaining three sensors enable to estimate the metal ions concentration. The analysis module (30) comprises a total of eight chambers, four chambers dedicated for Ni(II) analysis and the remaining four chambers being allocated for the Zn(II) analysis. In a possible embodiment, the SPCEs feature a carbon working electrode (WE) and counter electrode (CE) and a silver / silver chloride reference electrode (RE) were used as substrate for the Ni(II) and Zn(II) sensors.

[0079] In one of the preferred embodiments of the present invention, two chemical modifications of the WE for the specific detection of Zn and Ni were tested, one based on bismuth; and another based of dimethylglyoxime (DMG) complex, respectively.

[0080] For both modification strategies, several conditions were tested and optimized such as i) deposition techniques and conditions; ii) electrochemical cell configuration; iii) composition and properties of solutions; iv) measurement technique and respective conditions; v) compatibility of the presence of both metals (Zn and Ni) in the sample, and other possible interferent metals (Fe and Cu); and vi) interference of other substances used in the industrial process (such as from the washing of bath tanks).

[0081] In a preferred embodiment of the invention, for Ni(II) detection, DMG modified sensors (a reference coordination complex for the detection of Ni) were developed and optimized.

[0082] In a first stage, the SPCEs were scanned over fifteen DPV cycles in the range of -0.5V to -1.6V and followed by rising with Milli-Q water prior the modification with DMG. The WE was then modified by manually drop-casting 10 μL of 0.08 M of DMG (by twenty consecutive additions of 0.5μL) on the surface. The sensor surface was characterized using scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) where it was possible to confirm the modification of the sensor with the DMG, as can be seen in Figures 6.

[0083] Inductively coupled plasma atomic emission spectroscopy (ICP-OES) was used to validate the performance of the developed sensors and system. The samples were previously filter and pre-treated with acid to ensure complete dissolution of metals.

[0084] Cathodic stripping adsorptive voltammetry (CSV) was used to detect Ni(II) with the DMG modified sensors. A volume of 100 μL of Ni(II) in 0.1 M ammonium buffer pH 9.24 were dropped on the DMG sensor. The cathodic stripping adsorptive voltammetry (CSV) accumulation step was conducted by applying a constant potential -0.9 V over 2 min., following by a cathodic DPV stripping scan between -0.5 V and -1.6 V, with a pulse amplitude of 0.1 V and a period of 0.01 s, at a scan rate of 0.05 V s-1. The analytical performance of the Ni sensors showed a dynamic working range between 10 and 100 µg L-1with a detection limit of 2.7 µg L-1and a quantification limit of 9.2 µg L-1, as illustrated in.

[0085] After the dynamic range optimization, the developed sensors were tested with effluent samples from the entrance and exit of the wastewater treatment plant and the same were compared using ICP-OES and AAS. Results obtained from the three methods are shown in. The results obtained, by the developed sensor (DMG_SPCE@INL), are in accordance with the technical standard for metal analysis (AAS and ICP), as there are no significant differences between the results obtained.

[0086] For the Zn(II) detection, a bismuth modification (reference green modification for reaction with metals) was used. Similarly to the sensors prepared for Ni(II), the SPCEs are first scanned with five differential pulsed voltammetry (DPV) scans in the range of -0.5 V to -1.6 V in 0.1 M Ammonium Buffer pH 9.24. After this pre-treatment step, bismuth was electroplated ex-situ on the surface of the WE using 100 mL of 10 mM Bi(II) in 0.1 M nitric acid. A pulsed electrodeposition method (PED) was used to reduce the Bi(III) onto the WE surface, with the following pulse profile: -0.2 V deposition potential during 0.1 s, followed by open-circuit condition for 0.5 s, repeatedly until a total charge of -60 mC was reached. After the modification the sensors were rinsed with Milli-Q water and dry with nitrogen.

[0087] The sensor surface was characterized using SEM analysis, which allowed the observation of the formation of different structures depending on the conditions applied,.

[0088] Anodic stripping voltammetry (ASV) was used to detect Zn(II) using the Bi-modified screen-printed carbon electrodes (SPCE). 100 µL of Zn(II) in 0.1 M ammonium buffer pH 9.24 were dropped on the Bi-modified screen-printed carbon electrodes (SPCE). The anodic stripping voltammetry (ASV) accumulation step was conducted by applying a constant potential -1.6V for 30 s. After 10 s of equilibration time, an anodic differential pulsed voltammetry (DPV) stripping scan between -1.6V and -0.9V at a scan rate of 0.1 V / s stripped the adsorbed metal from the working electrode (WE) surface, with a pulse amplitude of 0.1V and a period of 0.01 s. The analytical performance of Zn sensors showed a dynamic working range between 100 and 1000 µg L-1with a detection limit of 29.9 µg L-1and a quantification limit of 99.7 µg L-1,.

[0089] After optimizing the dynamic range of the developed sensors, several samples of effluents from the WWTP were tested and validated using ICP-OES and AAS.shows the results of the analysis of effluent samples using the developed sensors (Bi_SPCE@INL), the analysis by ICP-OES and the analysis by AAS. It can be verified that the results obtained by the developed sensor are in accordance with standard techniques for metal analysis, as there are no significant differences between the results obtained.

[0090] CONTROL

[0091] To enable comprehensive analysis, the analytical platform (300) on the analysis module (30) integrates three potentiostats (300.1, 300.2, 300.3), to perform readings on the forty sensors integrated in the analysis module (30). To connect the potentiostats (300.1, 300.2, 300.3) to the sensors, the Zn cluster electrical connector and the Ni cluster electrical connector are connected by means of a multiplexing routing connector, as illustrated in. The multiplexing routing connector (301 and 302) comprises several analog multiplexers (for example DG419LDY and DG1409E, from Texas Instruments) routing the working electrode (WE), reference electrode (RE) and counter electrode (CE). There by the analysis module (30) is composed by the analytical platform (300;) which is assembled in a printed circuit board (PCB) which integrates the potentiostats (300.1, 300.2, 300.3) and the multiplexing routing connectors that connect the Zn sensor arrays (3011, 3012, 3013, 3014) and the Ni sensor arrays (3021, 3022, 3023, 3024), as shown in.

[0092] The analytical platform (300) integrates the three potentiostats (300.1, 300.2, 300.3) and provides a General-Purpose Input / Output (GPIO) interface used to select the desired sensor path by switching the multiplexer. Besides, two additional connection points allow to connect the analytic platform (300) to the multiplexing routing connector connectors (301 and 302).

[0093] CENTRAL CONTROL MODULE

[0094] The central control module (40) is used as primary control unit. This module manages the operation of the remaining modules in the system, which by its turn manage the submodules and subsystem included in its architecture. The primary functions of the central control module (40) are 1) the execution of a recipe, 2) managing communications and 3) perform data analysis.

[0095] The central control module (40) establishes connection to the sample processing module (20) and the analysis module (30) and coordinates the operation of the system. Besides, the central control module (40) provides a data analysis algorithm for analyzing the data measured by the analysis module (30), ensure the complete automation of the device.

[0096] CONTROLLING

[0097] In one of the preferred embodiments of the invention, the central control module (40) is composed by a single board computer (SBC) operating under a Linux based system. The central control module (40) is used as primary control unit and manages and synchronizes the operation of the other integral modules in the device, the analysis module (30) and the sample processing module (20). The modules are connected to the central control module (40) via serial protocols under a predefined set of commands that allow the central control module (40) to actuate over the modules.

[0098] The analysis module (30) connects to the central control module (40) through two distinct interfaces. Firstly, a USB interface is employed to control the analytical modules (EmStat4) via the MethodScript protocol (Palmsens BV). Secondly, a GPIO interface, consisting of 4 bits, selects the measurement path on the multiplexing routing connector submodule, enabling connection to the EmStat4 across the forty sensors installed in the device. The GPIO interface is directly controlled by the pin header of the SBC on the central control module (40).

[0099] For the sample processing module (20) the interface is directly establish via UART using the RX and TX GPIO pins on the pin header of the SBC on the central control module (40). The custom-made controller board of the sample processing module (20) sits on this header pin, establishing the necessary electrical connections to enable the communication. A predefined set of commands, composed of 12 bytes each, are used to allow the central control module (40) to actuate over the fluidic actuators connected to the sample processing module (20) controller. The commands are binary, the parameters are unsigned 8 bit or unsigned 32 bit for setting the control variable (working time, flowrate, on / off state, number of steps, etc.). Once the sample processing module (20) controller receives a command, it is processed and the MCU proceeds with the execution of the low-level routines to set the actuator.

[0100] DATA ANALYSIS

[0101] The central control module (40) is also responsible to estimate the levels of contaminates based on the data measured by the analysis module (30) and provide feedback to the sample processing module (20) according to the estimated values to adjust the sample preparation protocol if needed. This features an autoregulation of the system operation, allowing the system to adjust the analysis to the best conditions possible. For this, an algorithm for data analysis was developed and enables an automated “peak search” analysis on the voltammogram, current / potential curves, resulting from the stripping analysis voltammetry, performed by the analysis module (30) analytical platforms, and the successive concentration level estimation for Ni(II) and Zn(II), Figure 12A. The algorithm working principle is based on the curvature analysis of the current / potential curve.

[0102] In a preferred embodiment of the present invention, this process is conducted by a Python algorithm configured to assess the current spectrum obtained by the OEM modules and approximate the concentration of the desired metal ion. The algorithm implemented methodology is based on the evaluation of the changes on the voltammogram curvature to identify local minimums, which in turn serve as indicators for peaks. After the detection of the current peak, the developed software calculates the peak height which is proportional to the level of the metal concentration.

[0103] In this perspective, is worth to note that the shape of a peak has a pronounced curvature pointing downwards (i.e. negative concavity magnitude). By a similar analysis, the subsets corresponding to the base of the peak show a pronounced curvature pointing upwards (i.e. positive concavity magnitude). Therefore, by comparing the concavity of small subsets over the current / potential curve, the algorithm can identify the subset with most negative concavity, corresponding to the current peak, and the baseline is defined by the two subsets with most positive concavity. This is achieved by fitting second-degree equations to each subset and calculating its second derivative (i.e. calculate the concavity of each parabola over the entire curve),. Finally, the height between the peak and the defined baseline is calculated and the metal concentration is estimated proportionally.

[0104] The performance of the developed algorithm was evaluated by comparison with estimations achieved using a known state of the art software (commercial (Auto), Commercial (3pts)), whose peak height estimation was based on the manual extrapolation of the baseline. For this purpose, the comparison was performed on data collected by ad hoc modified SPCEs (n=30) employed for Ni(II) detection in water. The comparison is disclosed in.

[0105] CONNECTIVITY

[0106] To ensure connectivity with other systems the central control module (40) also supports Ethernet / IP based connections that allows it to be installed and controlled in other systems or networks as a peripheral device. This connection follows a Master-slave topology, the outer system acts as a master and the monitoring system acts as a slave. In this case the server sends a request to the client (monitoring system) and the client answers with the measured concentration results of Ni(II) and Zn(II). In a preferred embodiment of the invention, a Python script was used to configure and establish the Ethernet / IP client and manage the application-level routines according to an OSI model. A predefined set of commands was established between the WWTP Programmable Logic Controller (PLC) and the monitoring device to enable various functionalities: a) "reading" command for sample collection and performed a complete analysis; b) "reset" command for system rebooting; and c) "cleaning" command to initiate the cleaning sequence for the fluidic system. The device is set ready for the autonomous operation over 30 days, each measurement was triggered by the WWTP PLC. After this period some consumable should be replaced, and the reservoirs should be refilled with fresh solutions.

[0107] APPLICATION SOFTWARE

[0108] A tailored python library was developed to facilitate the integration of all the modules and allows to configure and manage the necessary communication protocols settings and command sets between the modules of the system. This library provides an abstraction for each of the modules and allows to easily communicate with the parts using user-friendly methods built around the configuration maps of each module.

[0109] A sequence-based approach was used to develop the controlling application. Several recipes were developed which performed specific and repetitive routines. These recipes can be triggered by the predefined user level commands via the IoT connectivity.

[0110] MONITORING SYSTEM OPERATION MODE

[0111] As described before, the monitoring system (10) is constituted mainly by three modules: 1) sample processing module (20), 2) analysis module (30) and 3) the central control module (40). An overall scheme of the monitoring system (10) is shown in.

[0112] The analysis sequence comprises the following steps: sample collection, filtration and storage in a reservoir. Next, analysis control is carried out to evaluate which dilution is most appropriate to ensure more accurate detection. To do this, the following operations are carried out, in a preliminary phase of diluting the sample by a factor of 1:100 and reading the sample. The sensor response is then checked for this dilution, if it is low, then the analysis is carried out using a dilution factor of 1:50. With the signal obtained from the electrochemical measurement of the sample, the concentration of the metal present in the sample is then estimated and this value is sent to the system for transmission over the Ethernet protocol. At the end, the system is washed with acid, water and air to avoid cross-contamination between the measurements of different samples. This procedure can be performed four times for each of the metals, Zn and Ni, as illustrated in.

[0113] 10 - monitoring system; 20 - sample processing module; 200 - water inlet; 201 - main pump; 202 - valve V1A of the range of first valves; 203 - valve V2A of the range of first valves; 204 - valve V3A of the range of first valves; 205 - valve V4A of the range of first valves; 206 - first filter set F1A; 207 - second filter set F2A; 208 - third filter set F3A; 209 - fourth filter set F4A; 210 - first reservoir of the set of reservoirs; 211 - second reservoir of the set of reservoirs; 212 - third reservoir of the set of reservoirs; 213 - fourth reservoir of the set of reservoirs; 214 - valve V1B of the range of second valves; 215 - valve V2B of the range of second valves; 216 - valve V3B of the range of second valves; 217 - valve V4B of the range of second valves; 218 - water output sample; 219 - stepper motor pump; 220 - aliquot sample; 221 - Zn sample valve; 222 - electrolyte buffer solution reservoir; 223 - electrolyte buffer solution; 224 - second stepper motor pump; 225 - Zn buffer valve; 226 - Ni buffer valve; 227 - Zn mixing reservoir; 228 - Zn recirculation brushed motor pump; 229 - Zn liquid recirculation; 230 - Ni Mixing Reservoir; 231 - Ni recirculation brushed motor pump; 232 - Ni liquid recirculation; 233 - Ni sample valve; 234 - acid reservoir; 235 - nitric acid; 236 - Air input; 237 - air valve; 238 - acid valve; 239 - water reservoir; 240 - water; 241 - water valve; 242 - input air valve; 243 - acid input valve; 246 - Nitric acid; 247 - Milli-Q Water; 248 - Ammonium buffer; 249 - Waste reservoir; 30 - analysis module; 300 - Analytic platform; 300.1 - First Potentiostat; 300.2 - Second Potentiostat; 300.3 - Third Potentiostat; 3011 - Zn Sensor Array 1; 3012 - Zn Sensor Array 2; 3013 - Zn Sensor Array 3; 3014 - Zn Sensor Array 4; 3021 - Ni Sensor Array 1; 3022 - Ni Sensor Array 2; 3023 - Ni Sensor Array 3; 3024 - Ni Sensor Array 4; 40 - central control module.Non Patent Literature

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Claims

Automated System (10) for heavy metal ions monitoring in water comprising a sample processing module (20); an analysis module (30); and a central control module (40); characterized by the central control module (40) being configured to monitor and quantify heavy metal ions in water by means of sample collection and processing, electrochemical analysis and data processing.Automated System (10) according to the previous claim, characterized by the sample processing module (20) comprising: a sample collection and filtration system; a sample dilution and mixing system; a cleaning system; and a control board configured to control and automate any of the previous systems.Automated System (10) according to any of the previous claims, characterized by the control board comprising a microcontroller unit (MCU) configured to integrate fluidic actuators as the range of first valves (202, 203, 204, 205) or a range of second valves (214, 215, 216, 217) or a main pump (201) or a stepper motor pump (224) or a Zn recirculation brushed motor pump (228) or a Ni recirculation brushed motor pump (231).Automated System (10) according to any of the previous claims, characterized by comprising an IoT interface configured to enable an installation and / or integration with local or remote data networks.Automated System (10) according to any of the previous claims, characterized by the analysis module (30) being configured to perform electrochemical analysis by means of a set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water.Automated System (10) according to any of the previous claims, characterized by the set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water comprising at least one Zn sensor and at least one Ni sensor configured to enable the evaluation of sample dilution conditions.Automated System (10) according to any of the previous claims, characterized by the set of sensors developed for the analysis of Ni(II) and Zn(II) levels in water comprising at least one Zn sensor and at least one Ni sensor configured to enable the estimation of metal ions concentration.Automated System (10) according to any of the previous claims, further comprising an analytical platform (300) characterized by integrating at least one potentiostat configured to perform readings from each of the Zn sensors through a Zn cluster.Automated System (10) according to any of the previous claims, characterized by the at least one potentiostat of the analytical platform (300) being configured to perform readings from each of the Ni sensors through a Ni cluster by means of multiplexing routing connectors.Automated system (10) according to any of the previous claims, characterized by the central control module (40) being configured to execute a data analysis using a peak search algorithm for the estimation of the metal ions, Zn and Ni, concentration by means of the recognition of a peak curvature on the current / potential curve.Automated System (10) according to any of the previous claims, characterized by the central control module (40) being configured to manage and control the sample processing module (20), and / or the analysis module (30).Operation method of the automated system (10) for water monitoring described in any of the previous claims characterized by comprising the steps of: collect water samples through a range of first valves (202, 203, 204, 205) which are connected to a main pump (201) configured to collect water samples from a water inlet (200); filter the water samples through a set of filters (206, 207, 208, 209) with different porosities; mixing the filtered water samples with an electrolyte solution, transferring filtered samples to the Zn mixing reservoir (227) or Ni Mixing Reservoir (230); recirculating the resulting solution in the mixing reservoir (227), ensuring a proper mixing of the electrolyte buffer solution (223) with the aliquot sample (220); measurement of the processed sample through an analysis module(30); analysis of the acquired data and selection of the appropriate dilution factor through the central control module (40); measurement of the processed sample through an analysis module(30); estimation of the concentration values of metal ions Zn and Ni through the central control module (40) using the data analysis algorithm; transmission of the calculated concentration through the communication interface; cleaning of the system through a cleaning system.Operation method according to the previous claim, characterized by comprising an electrochemical detection method based on a two-stage measurement including an accumulation step and a cathodic stripping differential pulsed voltammetry scan for the determination of Ni.Operation method according to the previous claim, characterized by comprising an electrochemical detection method based on two-stage measurement including an accumulation step and an anodic stripping differential pulsed voltammetry scan for the determination of Zn.

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