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Waste management is one of most important challenges in environmental protection. Much effort is put into the development of waste treatment methods for further use. A serious problem is the treatment of municipal sewage sludge. One method that is useful for this substrate is composting. However, it is reasonable to compost a sewage sludge mixed with other substrates, such as maize straw. To carry out the composting process properly, it is necessary to control some parameters, including the total solids and volatile solids content in the composted mixture. In this paper, a method for the determination of the total solids and volatile solids content based on image analysis and neural networks was proposed. Image analysis was used for the determination of the colour and texture parameters. The three additional features describing the composted material were percentage of sewage sludge, type of maize straw, and stage of compost maturity. The neural models were developed based on various combinations of the input parameters. For both the total solids and volatile solids content, the most accurate models were obtained using all input parameters, including 30 parameters for image colour and texture and three features describing the composted material. The uncertainties of the developed models, expressed by the MAPE error, were 2.88% and 0.59%, respectively, for the prediction of the total solids and volatile solids content.

To address some challenges of food security and sustainability of the poultry processing industry, a sequential membrane process of ultrafiltration (UF), forward osmosis (FO), and reverse osmosis (RO) is proposed to treat semi-processed poultry slaughterhouse wastewater (PSWW) and water recovery. The pretreatment of PSWW with UF removed 36.7% of chemical oxygen demand (COD), 38.9% of total phosphorous (TP), 24.7% of total solids (TS), 14.5% of total volatile solids (TVS), 27.3% of total fixed solids (TFS), and 12.1% of total nitrogen (TN). Then, the PSWW was treated with FO membrane in FO mode, pressure retarded osmosis (PRO) mode, and L-DOPA coated membrane in the PRO mode. The FO mode was optimal for PSWW treatment by achieving the highest average flux of 10.4 ± 0.2 L/m2-h and the highest pollutant removal efficiency; 100% of COD, 100% of TP, 90.5% of TS, 85.3% of TVS, 92.1% of TFS, and 37.2% of TN. The performance of the FO membrane was entirely restored by flushing the membrane with 0.1% sodium dodecyl sulfate solution. RO significantly removed COD, TS, TVS, TFS, and TP. However, TN was reduced by only 62% because of the high ammonia concentration present in the draw solution. Overall, the sequential membrane process (UF-FO-RO) showed excellent performance by providing high rejection efficiency for pollutant removal and water recovery.

Recirculating aquaculture systems (RAS) are efficient at solid waste capture and collection but generate a concentrated waste stream. Anaerobic digestion (AD) could be one potential treatment option for RAS facilities. However, the concentration of organic matter in the sludge can significantly affect the biogas quality from AD. This study evaluated the effect of fish sludge (FS) solid concentration on biogas quality. Three FS treatments consisted of different initial total solid concentrations (1.5%, 2.5%, and 3.5%) from a mixture of sludge produced by Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss). Methane (CH4) production was measured, quantified, and normalized on a volatile solids (VS) basis. The highest solid concentration treatment produced 23% more CH4 than the lowest solid concentration (519 mL/g VS versus 422 mL/g VS, respectively). Peak CH4 production occurred on Day 7 for the lowest FS concentration (78.2 mL/day), while the highest FS concentration peaked on Day 11 (96 mL/day). Peak hydrogen sulfide (H2S) concentrations ranged from 1803–2074 ppm across treatments, signifying the requirement of downstream unit processes for H2S removal from biogas. Overall, this study demonstrated that increasing the FS concentration can significantly enhance CH4 production without affecting the stability of the digestion process.

In the anaerobic digestion world, the dry matter (DM) and organic matter (OM) contents of the samples are major data for the design, handling, and maintenance of the processing of biogas plants. Volatile compounds, such as volatile fatty acids and ammonia, are likely to volatilize during drying at 105 °C and, thus, distort DM values; they have been described at length in the literature. On the other hand, the partial decomposition of bicarbonate, present in the digestion media, has been little described in the scientific literature. In this work, it appears that, in accordance with the stoichiometry of the partial decomposition of bicarbonate into CO2 and H2O, about 37% is volatilized as early as 70 °C and a fortiori at 105 °C, whether in aqueous solutions of pure bicarbonate or in different real digester media. With freeze-drying, no degradation of bicarbonate was observed, confirming the thermo-dependence of this reaction. This decomposition leads to an underestimation of DM measurements and some erroneous DM mass balances. It also led to an indirect overestimation of the OM, when expressed as a percentage of the DM.

Control of milk concentrate viscosity and total solids (TS) content prior to spray drying can improve dairy ingredient manufacture. However, the availability of hygienic and appropriately pressure rated process viscometers for inline monitoring of viscosity is limited. An acoustic flowmeter (FLOWave) is an inline process analytical technology (PAT) tool that measures changes in acoustic signals in response to changes in liquid properties (i.e., acoustic transmission (AT), acoustic impedance (AI), temperature and volume flowrate). In this study, an acoustic flowmeter is evaluated as an inline PAT tool for monitoring viscosity of milk protein concentrate (MPC85), protein and TS content of (MPC85), and standardised MPC (sMPC). Laboratory scale experiments were carried out at 45 °C for five different concentrations (4–21%) of MPC85 and sMPC. Results showed that AT decreased with an increase in MPC85 viscosity (e.g., AT was 98.79 ± 0.04% and 86.65 ± 0.17% for 4% and 21% TS content, respectively). Non-linear regression was carried out to develop a relationship between AT and offline viscosity (R2 (coefficient of determination) value = 0.97 and standard error of prediction = 1.86 mPa·s). AI was observed to increase at higher protein and TS content which was dependent on protein to total solid ratio (P_TSR). Multiple linear regression was carried out to develop the relationship between AI, protein content, TS content and P_TSR. Results demonstrated that AI could be used to monitor the protein and TS content of milk protein concentrate (R2 > 0.96). Overall this study demonstrated the potential of an inline acoustic flowmeter for monitoring process viscosity, protein and TS during dairy concentrate processing.

Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L−1 and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L−1 and less than 600 NTU, respectively and used rather than using whole dataset (R2 = 0.93 versus 0.88 for turbidity and R2 = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R2 = 0.66) was better than with the MR approach (R2 = 0.58), as expected due to the nonlinear nature of the transformation model.