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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.

Modern civilization is defined by the unsustainable consumption of most of the Earth’s natural resources and the contribution with different kinds of hazards to the air, water and soil. More than two centuries since the industrial revolution have caused the accu-mulation of different contaminants in different forms in most of the environmental compartments, therefore, there is an urgent need to shift this trend to sustainable solu-tions where protecting our environment needs to be in the core of new strategies. Waste management is one of the major areas where development towards sustainable devel-opment has a potential by implementing new strategies to protect the environment and also providing societies with secondary resources for the continuation of living on this Earth. In this chapter, an integrated waste management system based on the European hierarchy is introduced as an input towards reducing the impact of losing virgin mate-rials as wastes. In addition, international and European regulations and policies are also presented as a crucial tool to save the environment.

This article summarizes and performs a systematic analysis using experimental results from recent research on ammonium recovery from aqueous sources using biochar. Numerous studies have focused on using different materials to produce biochar adsorbents, and many have attempted to draw conclusions about the physical or chemical characteristics that dominate the adsorption to infer the mechanism. However, to date, there has not been statistical analysis performed on a large set of adsorption data and physical/chemical characteristics of chars to be able to draw conclusions about ammonium adsorption mechanisms. From this analysis, it was found that consistency in experimental methods and characteristic measurement reporting is lacking, and therefore it is difficult to perform metadata analysis and draw conclusions about ammonium adsorption on biochar. Among the important factors influencing ammonia recovery proposed in literature, the meta-analysis only strongly supports the effect of BET surface area and NH4+ concentration, with weaker support for the importance of cation exchange capacity and pyrolysis temperature. This suggests that standard procedures for biochar production, experiments and analysis of physical and chemical characteristics are needed to usefully compare results across different studies. Examples of the present difficulty in identifying trends across studies are shown by comparing clusters in the data identified by the analysis. The ability to make such comparisons would provide clearer direction in how best to further improve the adsorption capacity of biochars.

Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as setpoint tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.

The concerns about climate change, energy security and price fluctuation of fossil fuels are driving the growing interest in the development and utilization of renewable energy as a transportation fuel. In this aspect, the utilization of organic household waste for the production of biogas avoids the environmental impact of landfills. The further upgrading and utilization of biogas as a vehicle fuel avoids the environmental impact of fossil fuels. This paper presents the life cycle assessment of two utilization pathways of biogas produced from co-digestion of organic household waste, grease trap removal sludge and ley crops grown by local farmers. Specifically, this study assessed and compared the environmental impact of the production and utilization of bio-methane and biogas-based electricity as a vehicle fuel for public transport buses in Vasteras, Sweden. The system boundary for biogas production covered seven main steps: cultivation, harvesting and transport of ley crops, collection and transport of waste, pre-treatment and co-digestion of the substrate. The system boundary for bio-methane was further extended to account for the upgrading process and tailpipe emissions from combustion of bio-methane in the buses. In the case of biogas-based electricity, the system boundary was further extended to account for the combustion of biogas in the CHP unit and further utilization of electricity in the electric bus. The evaluation of the production routes showed that the methane losses and high energy consumption for both biogas production and upgrading process dominated the environmental impact of bio-methane production. However, the emissions from the CHP unit were solely responsible for the environmental impact of biogas-based electricity production. The functional unit identified for this study is 1 vehicle km travelled (VKT) of the bio-methane fuelled bus and electric bus. The global warming potential of the electric buses was 0.11 kg CO2-eq/VKT compared to 0.26 kg CO2-eq/VKT for the bio-methane buses. The electric buses could also reduce about half of the acidification and eutrophication impacts associated with the bio-methane fuelled buses. The lower fuel efficiency and high tailpipe emissions decreased the environmental advantages of the bio-methane buses. Eventually, this study ensures the biogas utilization which is environmentally sound and compares favourably with the alternative options. [GRAPHICS] .

Nitrogen recovery is the next step in the improvement of the wastewater treatment process, utilizing this important nutrient for fertilizers to decrease use of energy, petrochemicals, and impact on the environment. The majority of wastewater treatment plants currently employ methods to remove nitrogen which are energy intensive and have no additional benefits besides complying with effluent concentration limits. Instead, recovering nitrogen allows simultaneous treatment of wastewater while collecting a concentrated ammonia product, creating a circular economy solution. This review acts to compile current research regarding nitrogen recovery and compare different techniques' recovery efficiencies and energy requirements. One outcome of this review is that more than one third of the techniques reviewed had little comments around the energy question, and thus more research needs to take place as these recovery systems continue to evolve towards full scale implementation. Additionally, a basic economic analysis was completed to demonstrate potential investment opportunities to implement these technologies. From this investigation, gas permeable membrane technology has the potential to recover ammonia from wastewater using little energy and may provide a small income with the sale of the product. Other techniques such as vacuum membrane distillation with acid absorption need further validation to determine the energy costs, as the amount of heat recycling has a great impact on the overall energy and economic balances. Finally, a discussion of the misalignment of products from recovery techniques and fertilizers in use today highlights the lack of communication and information sharing between the research community and the end users. 

Process control is an important part of any industrial system. In a wastewater reuse system this remains true. Process monitoring and fault detection (FD) are important to ensure that the control system has access to reliable data which can be used in making decisions about the operation of the process. The reuse scenario being considered in this work is that of utilizing the nutrients from the wastewater as fertilizer to agricultural soil along with using the water for irrigation purposes. This paper identifies variables that are important to the control of the process and should be a focus of monitoring and FD. In wastewater treatment these variables include temperatures, pressures, liquid levels, flow rates, pH, conductivity, biomass content, suspended solids concentration, dissolved oxygen content, total organic carbon, and the concentrations of nitrate and ammonium. The variables of interest in the reuse of nutrients and water for agriculture include soil moisture, ambient conditions, plant height, biomass content, photosynthetic activity of the crop, leaf area and leaf water content, as well as the concentrations of several ions both in the soil and in the plant. Challenges associated with process monitoring and FD specific to the two processes are also discussed, examples of these are the high dimensionality of the problem, the harsh conditions that sensors must operate in and the non-linear relationships between variables. This information will be used in future work when comparing specific FD methods to ensure that methods chosen are capable of overcoming the commonly encountered problems.

Biomass is typically a material with highly variable properties making its use in industrial combustion processes challenging due to requirements on the steady operation. Property such as moisture content has an impact on fuel ignition characteristics and heat release from the biomass. Ash content negatively influences fluidization of the boiler bed and after-burning of small fuel particles, by forming an impermeable layer on the surface resulting in incomplete combustion and formation of harmful emissions.

The large variability of the properties thus creates undesired process instabilities which need to be addressed in a timely manner by appropriate operational/regulatory measures adjusting e.g. fluidization velocity, distribution of combustion air, under-pressure in the furnace etc. Consequently, there is a need for the implementation of sensors able to measure the properties of interest in real-time. In our previous studies, we demonstrated the ability of a single-point near-infrared sensor to measure fuel properties in real-time in a laboratory environment. However, we found that there is limited representativeness of the single-point measurements as also a cross-sectional variation of the fuel properties on the conveyor belt was apparent.

Therefore, the implementation of a sensor able to measure also a spatial distribution of the material in the biomass stream is suggested. Literature review shows that it can be achieved by the implementation of a near-infrared hyperspectral imaging camera.

The aim of the work is to present research activities at the Future Energy Center, Mälardalen University leading towards the installation of a) single-point and b) hyperspectral imaging near-infrared sensors for real-time moisture and ash content measurements. The study further presents the concept of NIR sensors integration for process optimization and the introduction of new advanced control concepts for steam boilers.