CO2 Sensor Research Articles

Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Perancangan Sistem Pendeteksi Polusi Udara Di Dalam Kamar Mesin Secara Wireless Menggunakan Sensor MQ135 Dan TGS2106 Berbasis Lora Ra-02

Air pollution is a problem that needs attention because it can threaten human life. Many human activities cause air pollution. Especially in closed rooms such as ship engine rooms. Therefore, an air pollution detection tool is needed to determine the air pollution index in that place in order to maintain pollution levels below the threshold value. Therefore, it is necessary to have a tool to detect air pollution using the MQ-135 gas sensor to detect carbon dioxide (CO2) gas and TGS2106 to detect nitrogen dioxide (NO2) gas. The type of research method used is the experimental research method. After designing the tool, testing the tool, the air pollution detection system in the engine room wirelessly using the MQ135 and TGS2106 sensors based on LoRa Ra-02 had an average error rate of 1.3% for the MQ135 sensor reading while the TGS2106 sensor reading had an average level. -average error of 6%. Therefore, the Air Pollution Detection System in the Engine Room Wirelessly Using the MQ135 and TGS2106 Sensors Based on LoRa Ra-02 can work well.

Developing a Chemiresistive Gas Sensor Array for Simultaneous Detection of Ammonia and Carbon Dioxide Gases.

Chemiresistive polymer-based sensors are promising platforms for monitoring various gases and volatile organic compounds. While they offer appealing attributes, such as ease of fabrication, flexibility, and cost-effectiveness, most of these sensors have a nearly identical response to cross-reactive gases, such as ammonia (NH3) and carbon dioxide (CO2). Aiming to address the shortcomings of chemiresistive polymer-based sensors in selectivity and simultaneous measurements of cross-reactive gases, a chemiresistive sensor array was developed consisting of components sensitive to carbon dioxide and ammonia as well as a control segment to provide the baseline. The designed system demonstrated a wide detection range for both ammonia (ranging from 0.05 to 1000 ppm) and carbon dioxide (ranging from 103 to 106 ppm) at both room and low temperatures (e.g., 4 °C). Our results also demonstrate the ability of this sensor array for the simultaneous detection of carbon dioxide and ammonia selectively in the presence of other gases and volatile organic compounds. Finally, the array was used to monitor CO2/NH3 in real food samples to demonstrate the potential for real-world applications.

Pd and Pd-B modified g-CN monolayer as innovative sensor and scavenger for CO, NO2, C2H2 and C2H4: A DFT study

The prompt detection of toxic gases like CO, NO2, C2H2, and C2H4 is crucial for both environmental preservation and industrial safety. In this work, we investigate the adsorption behavior and sensing performance of these four gases on both intrinsic g-CN and g-CN modified with single Pd atom and Pd-B pair, denoted as Pd-g-CN and Pd-B-g-CN, respectively, utilizing the first-principles calculations. The results indicate that the introduction of Pd atom and Pd-B pair significantly enhances the adsorption effect of g-CN monolayer toward these gases, with adsorption energies ranging from − 0.44 eV to − 1.43 eV. Furthermore, all adsorptions are identified as chemisorption, with the adsorption strength following the order of NO2 > CO > C2H4 > C2H2. Additionally, the adsorption of NO2, C2H2, and C2H4 induces notable changes in the work function of Pd-g-CN and Pd-B-g-CN, as well as approximately a 20-time increase in the bandgap of Pd-g-CN. Moreover, the Pd-g-CN and Pd-B-g-CN monolayers exhibit reasonable recovery times for C2H2 and C2H4, measured as 0.04 s and 6.40 μs at 298 K, respectively. Furthermore, NO2 can be rapidly desorbed from the Pd-B-g-CN surface at 398 K, with a short recovery time of 8.17 s. Therefore, the Pd-g-CN and Pd-B-g-CN monolayers can be regarded as potential gas-sensitive materials for the detection of C2H2 and C2H4 at room temperature, respectively. These research findings provide robust theoretical support for the design of novel gas sensors and efficient toxic gas adsorbents.

A Retrospective Analysis of Indoor CO2 Measurements Obtained with a Mobile Robot during the COVID-19 Pandemic.

This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

A selective fluorescent probe derived from benzo[d]imidazole for carbon dioxide detection and bioimaging

In this work, a benzo[d]imidazole-derived fluorescent probe, DCBIF, was developed for the carbon dioxide (CO2) detection and imaging in living lung cells. On the basis of investigating previous reports and modifying the test method, the in-solution experiments showed that DCBIF exhibited the fluorescence response at 420 nm to CO2 under the excitation of 350 nm. The response was steady and rapid in physiological conditions. The linear range of the detecting system covered the practical necessity, and the limit of detection was 0.002 mL/min. There were few reports for detecting CO2 depicted the intracellular imaging while the cells were not pulmonary. Herein, in all the tested groups including oxygen-rich, hypoxia, and hypoxia recovered, DCBIF realized the imaging of the CO2 level in living A549 lung cells. This work was meaningful for developing fluorescent implements and imaging scenarios.

CO2 response screen in grass Brachypodium reveals the key role of a MAP kinase in CO2-triggered stomatal closure.

Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized B. distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a "chill1" mutant exhibited cooler leaf temperatures than wild-type Bd21-3 parent control plants after exposure to increased CO2. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3- sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.

High-Sensitivity Differential Helmholtz Photoacoustic System Combined with the Herriott Multipass Cell and Its Application in Seed Respiration.

A highly sensitive photoacoustic detection system using a differential Helmholtz resonator (DHR) combined with a Herriott multipass cell is presented, and its implementation to sub-ppm level carbon dioxide (CO2) detection is demonstrated. Through the utilization of erbium-doped optical fiber amplifier (EDFA), the laser power was amplified to 150 mW. Within the multipass cell, a total of 22 reflections occurred, contributing to an impressive 33.6 times improvement in the system sensitivity. The normalized noise equivalent absorption coefficient (NNEA) was 8.64 × 10-11 cm-1·W·Hz-1/2 [signal-to-noise ratio, (SNR) = 1] and according to the Allan variance analysis, a minimum detection limit of 500 ppb could be achieved for CO2 at 1204 s, which demonstrates the long-term stability of the system. The system was applied to detect the respiration of rice and upland rice seeds. It is demonstrated that the system can monitor and distinguish the respiration intensity and respiration rate of different seeds in real time.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

First-principles exploration of N- and Pd-doped graphene as CO and NO2 gas sensor for operation status evaluation in AIS

To evaluate the operation status of air insulated switchgears (AIS), this work purposes N- and Pd- embedded graphene (N- and Pd-graphene) as potential gas sensors upon two typical faults gases (CO and NO2) from the first-principles simulations. It is found that the N and Pd atoms can be stably trapped on the C-vacancy of the C-defected graphene with the formation energy of −12.17 and −5.12 eV, respectively. N-graphene behaves physisorption towards CO and NO2 molecules while Pd-graphene behaves chemisorption instead. The resistance-type and work function (WF)-based sensing mechanisms of N- and Pd-graphene upon such two gas species are illustrated and uncovered by analyzing their deformations of electronic property and WF in the gas adsorption systems, which reveals the potential of Pd-graphene as a resistive CO and NO2 sensor, N-graphene as a resistive NO2 sensor, as well as the N- and Pd-graphene as WF-based gas sensor for NO2 detection. This work highlights the comparison of adsorption and sensing performances between N- and Pd-graphene upon two typical gas sensors in AIS, which would be meaningful to explore novel graphene-based sensing materials facilitating their investigations and applications in the power system.

Assessing the gas sensing capability of undoped and doped aluminum nitride nanotubes.

CO2 and CO gas sensors are very important to recognize the insulation situation of electrical tools. ToCO explore the application of noble metal doped of aluminum nitride nanotubes for gas sensors, DFT computations according to the first principal theory were applied to study sensitivity, adsorption attributes, and electronic manner. In this investigation, platinum-doped aluminum nitride nanotubes were offered for the first time to analyze the adsorption towards CO2 and CO gases. Firm construction of platinum-doped aluminum nitride nanotubes (Pt-AlNNT) was investigated in four feasible places, and the binding energy of firm construction is 1.314 eV. Respectively, the adsorption energy between the CO2 and Pt-AlNNT systems was - 2.107 eV, while for instance of CO, the adsorption energy was - 3.258 eV. The mentioned analysis and computations are considerable for studying Pt-AlNNT as a new CO2 and CO gas sensor for electrical tools insulation. The current study revealed that the Pt-AlNNT possesses high selectivity and sensitivity towards CO2 and CO. In this research, Pt-doped AlNNT (Pt-AlNNT) has been studied as sensing materials of CO and CO2 for the first time. The adsorption process of Pt-AlNNT has been computed and analyzed through the DFT approach. DFT computations by using B3LYP functional and 6-31 + G* basis sets have been applied in the GAMESS code for sensing attributes, which contribute to potential applications.

Mining Metal‐Biomolecular Framework/PANI Composite CO Sensor: Material Synthesis, Gas Sensing Performance and Mechanism

AbstractIn addressing the issue of poor gas sensitivity in semiconductor CO sensor materials used in mining, such as pure PANI. This study utilized an ice‐water bath in combination with an in‐situ polymerization method to synthesize a new composite material consisting of Cu(II)‐racemic glutamate nanofibers and PANI. The morphology, structure, and chemical bonds of the composite material were analyzed using scanning electron microscopy‐energy dispersive spectroscopy (SEM‐EDS) and spectral analysis. The material's ability to detect CO (100–500 ppm) at room temperature was investigated, revealing a response value of up to 6.8 % for 400 ppm CO detection when the composite material was deposited on a ceramic interdigital electrode. The material exhibited good selectivity and responsiveness to CO detection at room temperature, attributed to the heterojunction formed between Cu(II)‐racemic glutamate nanofibers and PANI, which enhanced carrier migration. Consequently, the Cu(II)‐racemic glutamate nanofiber/PANI composite(Cu(II)‐RGlu/PANI) material shows promise as a high‐performance sensing material for CO detection at room temperature and as a low‐power, integrable sensor material for underground coal mines. This research contributes to the development of a theoretical framework for such applications.

Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use.

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7-4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from -0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from -1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0-3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm.

Enhanced CO sensor based on In2O3 nanocubes with decoration of Au under high humidity: Experimental combined with theoretical study

Au-modified In2O3 nanocubes with varying Au content (0.7, 1, 2, 3, 10 mol%) were prepared via a simple hydrothermal method and in-situ reduction. The morphology of Au-modified In2O3 was consistent with pure In2O3, both exhibiting cubic structures composed of ∼31 nm particles. Gas sensing tests revealed that compared to pure In2O3, 1 mol% Au-modified In2O3 nanocubes exhibited a 40 °C lower optimal operating temperature (OOT), and a higher response value of 4.88 to 100 ppm CO. Additionally, Au/In2O3 sensors showed significantly less influence from humidity (75 %, 85 %) than pure In2O3 sensors and maintained CO sensing ability under high humidity conditions. First-principles calculations of the adsorption energies of CO molecules on In2O3 and Au/In2O3 surfaces were further performed to investigate the gas sensing mechanism. The results indicated that the improved CO sensing performance of Au/In2O3 sensors can be attributed to the chemical sensitization effects of Au.

Prehospital Surgical Cricothyrotomy in a Ground-Based 9-1-1 EMS System: A Retrospective Review.

Airway management is a cornerstone in the prehospital care of critically ill or injured patients. Surgical cricothyrotomy offers a rapid and effective solution when oxygenation and ventilation fail using less-invasive techniques. However, the exact indications, incidence, and success of prehospital surgical cricothyrotomy are unknown, with variable rates reported in the literature. This study aimed to examine prehospital indications and success rates for surgical cricothyrotomy within a large, suburban, ground-based Emergency Medical Services (EMS) system. This is a retrospective analysis of 31 patients who underwent paramedic performed surgical cricothyrotomy from 2012 through 2022. Key demographic parameters were analyzed, including the incidence of cardiac arrest, call type (trauma versus medical), initial airway management attempts, number of endotracheal intubation (ETI) attempts before surgical airway, and average time to the establishment of a surgical airway in relation to the number of ETI attempts. Surgical cricothyrotomy success was defined as the acquisition of four-phase end-tidal capnography reading. The primary data sources were the EMS electronic medical records, and descriptive statistics were calculated. A total of 31 patients were included in the final analysis. Of those who received a surgical cricothyrotomy, 42% (13/31) occurred in the trauma setting, while 58% (18/31) were medical calls. In all patients who underwent surgical cricothyrotomy, the median (IQR) time to the procedure was 17 minutes (IQR = 11-24). In trauma patients, the median time to surgical cricothyrotomy was 12 minutes (IQR = 9-19) versus 19 minutes (IQR = 14-33) in medical patients. End-tidal carbon dioxide (ETCO2) detection and placement success was confirmed in 94% (29/31) of patients. Endotracheal intubation was attempted in 55% (17/31) before subsequent surgical cricothyrotomy, with 29% (9/31) receiving more than one ETI attempt. The median time to surgical cricothyrotomy when multiple prior intubation attempts occurred was 33 minutes (IQR = 23-36) compared to 14.5 minutes (IQR = 6-19) in patients without a preceding intubation attempt. Prehospital surgical airway can be performed by paramedics with a high degree of success. Identification of the need for surgical cricothyrotomy should be determined as soon as possible to allow for rapid securement of the airway and to ensure adequate oxygenation and ventilation.

Development of carbon dioxide sensor using fiber optic-based refractometer

In this work, a fiber optic-based refractometer has continuously been developed for measuring carbon dioxide (CO2) using the evanescent wave method. Consequently, this technique is utilized to determine the light absorption values. Further, the chemical compound is also used to coat onto the modified sensing probe. However, this probe is, generally, made from multi-mode fiber (MMF). Moreover, the principle of altering the refractive index (RI) value is applied by removing the cladding of the sensing probe using hydrofluoric acid (HF). In addition, the zinc oxide (ZnO) mixed with the sodium silicate (Na2SiO3), known for its CO2 absorption capabilities, is used as a compound to coat the modified cladding. Besides, the sensing probe has been developed in three selected lengths (L) which are 10, 20, and 25 mm, and then tested from 0 to 40 % of CO2 concentration. Moreover, a biogas analyzer has been operated as a standard reference instrument. Afterward, an engineering application software is also developed for data processing to analyze the data in the relationship between the differential voltage, percentage of attenuation, and percentage of concentration versus time domain, respectively. The experimental results reported that the developed sensing can detect CO2 in any sensing length from 8.11 to 38.25 % with a sensitivity of 0.0102 mV/%concentration. In addition, a percentage attenuation of 2.11 %, 14.52 %, and 29.29 % is achieved in whole sensing lengths. However, this analyzes that at 10 mm the probe is more suitable due to its high responsibility to detect the CO2. Furthermore, the experimental results showed that the selected compound can recover with a recovery time of approximately 6 min.

Boron-nitride nanostructures for the detection of harmful gases (CO, CO2, H2S, N2O, and SO2)

Boron-nitride (BN) structures are considered materials with several applications, including sensing platforms. Through density functional theory calculations based on the PBE-D3/6-311G(d,p) level of theory, we propose the boron-nitride nanosheet, armchair nanotube (5,5), and fullerene as possible sensors for the harmful gases CO, CO2, H2S, N2O, and SO2. According to our results, the systems exhibit adsorption energies of −0.10 eV to −1.26 eV, and remarkable electronic parameters such as chemical potential, energy gap, and HOMO and LUMO energies, ideal for serving as chemical sensors of such pollutant gases. The nanostructures can be selective to sense a particular gas, mainly CO, N2O, and SO2. Additionally, we note a dramatic change in the electrical conductivity up to 2.14 eV, and low recovery time from 15 μs to 6.2 s. Finally, the BNNS-SO2, BNNT-N2O, BNNT-SO2, and BNF-CO are the more promissory systems to be used as chemical sensors of SO2, N2O, and CO.

EC-QCL sensing system–incorporated adaptive baseline correction algorithm for simultaneous detection of multiple gas components

In this study, we presented a multi-gas sensor developed by employing an external cavity quantum cascade laser (EC-QCL)-integrated adaptive baseline correction algorithm for the simultaneous detection of ammonia (NH3), ozone (O3), and carbon dioxide (CO2). The EC-QCL sensing system was selected due to its high wavelength resolution and wide tuning range (1033–1068 cm−1), enabling the simultaneous measurement of multi-gases. The system complexity was effectively decreased and the superimposed noise was avoided by employing an adaptive baseline correction algorithm based on differential transformation and B-Spline approximation (ADTBA), which enabled the resolution of the uneven EC-QCL baseline signals. The performance validation of the ADTBA algorithm by comparing it with the conventional polynomial fitting for detecting the spectra of NH3, O3, and CO2 showed that the former outperformed the latter. The results showed a sensitivity enhancement factor of 3.7, indicating that the newly developed algorithm can generate a high-quality gas absorption spectrum. Allan deviation analysis of the system's responsiveness revealed an averaging time of 120 s resulting in sensitivities of 1.3, 26.7, and 37.0 ppm for NH3, O3, and CO2, respectively.

Simultaneous thermal zoning and demand control ventilation

Heating, Ventilation, and Air Conditioning systems play a crucial role in controlling indoor air conditions, including temperature, humidity, and CO₂ concentration, to ensure human comfort and safety. Thermal zoning and demand-controlled ventilation have demonstrated enhancements in comfort and energy efficiency. This study aims to develop a mathematical model for a Heating, Ventilation, and Air Conditioning system employing an estimated state-feedback control algorithm to optimize indoor comfort and energy savings while minimizing sensor requirements. The model encompasses dynamic equations governing CO₂ concentration and temperature in a five-room building. Temperature regulation is achieved by adjusting the airflow from a cooling unit with dampers, while CO₂ levels are managed by controlling the proportion of fresh air intake. To showcase the advantages of the proposed control approach, comparisons were made with alternative methods based on response performance, energy consumption, and sensor requirements. The estimated state-feedback control outperformed other approaches, requiring only one temperature and one CO₂ sensor at the mixed return. Additional occupancy sensors in each room are not necessary if an Extended Kalman filter is utilized. This technique exhibits scalability, adaptable to varying output variables and room configurations, and holds promise for broader implementation in the HVAC industry. By optimizing energy consumption and maintaining indoor air quality, this approach aligns with established sustainability metrics, promoting environmental responsibility. Compliance with health and safety audit standards ensures the fulfillment of social targets, while adherence to regulations and laws ensures governance targets are met.