https://www.mdu.se/

Efficient characterization of wastewater stream quality is vital to ensure the safe discharge or reuse of treated wastewater (WW). There are numerous parameters employed to characterize water quality, some required by directives (e.g. biological oxygen demand (BOD), total nitrogen (TN), total phosphates (TP)), while others used for process controls (e.g. flow, temperature, pH). Well-accepted methods to assess these parameters have traditionally been laboratory-based, taking place either off-line or at-line, and presenting a significant delay between sampling and result. Alternative characterization methods can run in-line or on-line, generally being more cost-effective. Unfortunately, these methods are often not accepted when providing information to regulatory bodies. The current review aims to describe available laboratory-based approaches and compare them with innovative real-time (RT) solutions. Transitioning from laboratory-based to RT measurements means obtaining valuable process data, avoiding time delays, and the possibility to optimize the (WW) treatment management. A variety of sensor categories are examined to illustrate a general framework in which RT applications can replace longer conventional processes, with an eye toward potential drawbacks. A significant enhancement in the RT measurements can be achieved through the employment of advanced soft-sensing techniques and the Internet of Things (IoT), coupled with machine learning (ML) and artificial intelligence (AI).

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet thegrowing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged aspromising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneousproduction, and reducing agro-waste remain substantial challenges in these production systems. One potentialsolution is the use of optical sensing technology, which can provide real-time data to help growers makeinformed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluatecrop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematicliterature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoorfarming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 toidentify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations andinspirations for possible new research directions. The results of this review will contribute to the development ofsustainable and efficient methods of food production.

Accurate and rapid determination of moisture content is essential in crop production and decision-making for irrigation. Near-infrared (NIR) spectroscopy has been shown to be a promising method for determining moisture content in various agricultural products, including herbs and vegetables. This study tested the hypothesis that NIR spectroscopy is effective in accurately measuring the moisture content of Genovese basil (Ocimum basilicum L.), with the objective of developing a respective calibration model. Spectral data were obtained from a total of 120 basil leaf samples over a period of six days. These included freshly harvested and detached leaves, as well as those left in ambient air for 1–6 days. Five spectra were taken from each leaf using a handheld NIR spectrophotometer, which covers the first and second overtones of the NIR spectral region: 950–1650 nm. After the spectral acquisition, the leaves were weighed for fresh mass and then put in an oven for 72 h at 80 °C to determine the dry weight and calculate the reference moisture content. The calibration model was developed using multivariate analysis in MATLAB, including preprocessing and regression modeling. The data obtained from 75% of the samples were used for model training and 25% for validation. The final model demonstrates strong performance metrics. The root mean square error of calibration (RMSEC) is 2.9908, the root mean square error of cross-validation (RMSECV) is 3.2368, and the root mean square error of prediction (RMSEP) reaches 2.4675. The coefficients of determination for calibration (R2C) and cross-validation (R2CV) are consistent, with values of 0.829 and 0.80, respectively. The model’s predictive ability is indicated by a coefficient of determination for prediction (R2P) of 0.86. The range error ratio (RER) stands at 11.045—highlighting its predictive performance. Our investigation, using handheld NIR spectrophotometry, confirms NIR’s usefulness in basil moisture determination. The rapid determination offers valuable insights for irrigation and crop management.

The atmospheric carbon dioxide (CO2) concentration has been progressively increasing since the onset of the Industrial Revolution and has already reached at around 420 μmol mol⁻¹ nowadays. It is well recognized that elevated CO2 concentration stimulates the yield for C3 crops, but it also simultaneously changes the essential nutrients. However, compared with the main crops, far less attention has been devoted to the effects of elevated CO2 concentration on vegetable growth and quality. Vegetables are highly recommended in daily diets due to their diverse range of beneficial compounds, such as vitamins, antioxidants, minerals, and dietary fiber.  In controlled greenhouse vegetable cultivation, elevated CO2 has been widely adopted as an agricultural practice for enhancing plant growth. Thus, understanding both vegetable growth and nutrient status is crucial to assess the potential impacts of elevated CO2 on future food security in both natural and controlled environments. However, much more attention has been paid to biomass enhancement, and elevated CO2 effects on nutrient quality are less recognized. Among the nutrients, Zinc (Zn) and Iron (Fe) are essential elements in humans. Previous studies have demonstrated a decreasing trend of Zn and Fe in main crops such as wheat and rice with increased CO2, while less is known about whether this alleviation effect on Zn and Fe can apply to vegetables. Therefore, a meta-analysis was conducted in this study to evaluate the influence of elevated CO2 concentration in the atmosphere on vegetable Fe and Zn status, and quantify the potential impact of future climate on nutrition security.

Integrating CO₂ capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO₂ concentration of the flue gas (FG), which further affect the operation of post-combustion CO₂ capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO₂ concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO₂ concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO₂ concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships.

This study conducts a review of the current practices of bioeconomy education and training in the EU; as well as the associated methodologies; techniques and approaches. In recent years; considerable efforts have been made towards developing appropriate bioeconomy education and training programs in order to support a transition towards a circular bioeconomy. This review separates bioeconomy education approaches along: higher education and academic approaches, vocational education and training (VET) and practical approaches, short-term training and education approaches, and other approaches. A range of training methodologies and techniques and pedagogical approaches are identified. The main commonalities found amongst these approaches are that they are generally problem based and interdisciplinary, and combine academic and experiential. Higher education approaches are generally based on traditional lecture/campus-based formats with some experiential approaches integrated. In contrast, VET approaches often combine academic and practical learning methods while focusing on developing practical skills. A range of short-term courses and other approaches to bioeconomy education are also reviewed.

Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

To achieve net-zero emissions by 2045 in Sweden, bioenergy with carbon capture and storage (BECCS) has been identified as a key technology. Biomass fired combined heat and power plants (bioCHPs) constitute the second largest CO2 emission source after paper and pulp plants. Therefore, integrating BECCS in bioCHPs will contribute significantly to achieve Sweden’s climate goal. In the prerequisite of maintained heat generation for district heating (DH) sectors, this paper aims to estimate the aggregated negative emissions when integrating CO2 capture into existing 110 bioCHPs, in which the boiler load can be increased to the maximum capacity. A physical model was developed for bioCHP, and the operation of an example bioCHP can be determined by the objective function of maximizing the heat for CO2 capture. Based on results of example plant, the artificial neural network models were further established to predict the capture performance from other plants. Not only the amount of captured CO2, but also the amount of avoided CO2 was examined for a better understanding of the contribution of negative emissions. It is estimated that the heat generation used for DH is 33925.83 GWh/year. The aggregated amount of captured CO2 is estimated of 23.11 Mton/year; the aggregated amount of avoided CO2 is estimated of 20.22 Mton/year. The electricity generation is found to be decreased by 8810.82 GWh/year (63.6%) when BECCS is included.

This paper presents an overview of different methods used in what is normally called AI-methods today. The methods have been there for many years, but now have built a platform of methods complementing each other and forming a cluster of tools to be used to build “learning systems”. Physical and statistical models are used together and complemented with data cleaning and sorting. Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control, maintenance on demand and production planning. In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications.

For decades, thermal imaging technology serves as an effective tool and is implemented in many industrial and commercial applications, including energy and building sectors. Recent trends in the field further show an increased interest in aerial thermal imaging applications that brings new opportunities toward sustainability. Unmanned aerial vehicles (UAVs) (i.e., drones) equipped with thermal cameras are currently used for building thermography inspection, which is a crucial technology to accelerate the identification of CO2 mitigation within the building sector to tackle the global goals (SDG 11 target 11.6 and SDG 7 target 7.3). This study presents an evaluation of a low-cost, visual-thermal drone system for building thermography inspection (SDG 9 target 5.5). The evaluation was limited to the thermal imaging potentials of the system. The UAV system is used to examine its capacity to detect various heat loss, including insulation defects, air/ water leakage, and validation of different suspected energy loss case studies. The examination also involves the evaluation of the cost-effectiveness of the thermal-drone system. The thermography inspection was carried out on several buildings with different sizes, types, and activities they are used for. Therefore, the detection/identification tasks for the thermal-drone system were different from an inspection to another. This study aimed to identify different limitations and advantages of using such a low-cost thermal-drone system for building thermography inspection. The technical evaluation was based on several criteria, including fly duration, stability, image quality, data flexibility, integration potentials, etc. Additionally, the cost-effectiveness and other practical aspects were considered in the evaluation. The results show a combination of both limitations and advantages of adopting such a low-cost drone system. In contrast to the supplier's description, the thermal image data are not a radiometric JPG file that significantly limits quality and opportunities. Accordingly, the thermal image gives a standard JPG file and does not provide a temperature distribution to make any post-analysis processing or post-editing presentations. This issue can be solved partially, as the live thermal images provide a temperature distribution that allows different utilizations, e.g., identifying temperature spots, which can be included in a screenshot of the drone screen controller. Furthermore, the image data's limitations do not allow 3D modelling of the building objects which is possible for the radiometric image files. The image resolution and accuracy are limited; however, short distance inspections provide good image qualities. The results reveal that the thermal drone system can detect common insulation issues such as missing insulation and clear energy loss. However, the capacity is limited in regards to high accuracy demand and more in-depth data analysis. In conclusion, the examined drone system is a cost-effective tool for DIY use and superficial aerial building thermography inspection (SDG 11 target 11.6). Therefore the suggested system is not sufficient for higher demand and more professional inspections. The suggested proposal is an effective method to identify CO2 mitigation potentials within the buildings that are significantly promoting the achievement of some SDGs. Additionally, the inspection method can be conducted remotely, keeping social distancing in the time of pandemics.