A real case study of a full-scale anaerobic digestion plant powered by olive by-products

The anaerobic digestion plant studied in this paper is one of the first full-scale plants using olive oil by-products. This is a two-stage plant with a power of 100 kWe. Two tests were performed: the first on olive pulp and pitted pomace and the second on biomass consisting of 10% crushed cereal. In both cycles, the retention time was 40 days. The production of biogas was between 51 and 52 m3/h, with limited fluctuations. The specific production values of biogas indicate that a volume of biogas greater than 1 m3/kg was produced in both tests. The produced biogas had a methane percentage of about 60% and the specific production (over total volatile solids, TVS) of methane was of the order of 0.70 m3methane/kgTVS. FOS/Alk (ratio between volatile organic acids and alkalinity) was always lower than 1 and tended to decrease in the second digester, indicating a stable meth-anogenic phase and the proper working of the methanogenic bacteria in the second reactor. The concentration of incoming biomass TPC (total polyphenols content) can vary significantly, due to the seasonality of production or inadequate storage conditions, but all measured values of TPC, between 1840 and 3040 mg gallic acid kg−1, are considered toxic both for acidogenic and methano-genic bacteria. By contrast, during the process the polyphenols decreased to the minimum value at the end of the acidogenic phase, biogas production did not stop, and the methane percentage was high

New modelling approach for the energy and steam consumption evaluation in a fresh pasta industry

The agri-food industry has a fundamental role in the Italian and European economy and is characterized by the need to reduce energy costs and emissions. Therefore, it is essential for food companies to give due consideration to the energy efficiency of the processes, to reduce production costs, without sacrificing the quality of primary production and maintaining adequate levels of competitiveness on the market. In this study, a theoretical and experimental mass and energy balance of the production process of fresh pasta was made, also considering the energy contributions of a cogeneration plant recently built in the company subject of the experimental study. The final aim was to determine scientific values of specific energy consumption for this type of production by mean a new modelling approach. The mass and energy balances were carried out for the production line of fresh semolina pasta, as well as for the cogeneration plant; monitoring the flows of raw materials and steam that characterize the production process. The results of this study can be generalized to all production processes of the same type and, in the specific case, constitute a decisive logical step for the definition of the energy recovery solutions to be adopted in the company studied, in relation to their economic-production needs

A new tool for food industrial plant simulation and IoT control

The general objective of this research is to create a Simulation Software allowing the Optimization and Control of an IoT system for food industry applications. An industrial plant is today considered a complex system as it is often composed of many types of machines characterized by a strong temporal variation of the productions. The three missing factors to accomplish what above are: • Simulation; • Implementation of communicating objects also called IoT (Internet of Things) systems. • Intelligent control; In this paper, we show the tools developed and implemented in the Simulation software used to model a physical industrial process by mean of a user-friendly graphic interface allowing interactively defining different plant configurations, through the selection of basic graphic elements and their appropriate connection. The software has been developed in Unity, a cross-platform engine used to create three-dimensional, twodimensional, virtual reality, and augmented reality, as well as simulations. The software consists of the following main sections: • library; • creation and management of the process scheme; • simulation. The "library" section contains the elements (blocks) used in the process scheme, to be completed with functions describing their specific behaviour. These elements represent physical components and logical connectors that allow connecting the different blocks generating an oriented graph.The "creation and management of the process scheme" section is the graphical interface through which new configurations can be created. In particular, the connectors correspond to a flow of information from one block to another and contain the output variables determined by the function of the starting block. These values are used as input in the arrival block. The "simulation" section allows simulating of previously designed configurations

Experimental trials and dynamical simulation of the potential biogas production in a frozen food industry

Food industry determines the production of huge quantities of by-products, solid and waste. Identifying an ecological and economically viable solution for the management and disposal of horticultural wastes helps reducing the environmental impact of this kind of industries. In this paper a biogas production potential was obtained during one year of hourly data observation in a frozen food industry through dynamical simulation. This provides useful data for the correct management of an anaerobic digestion plant using the horticultural wastes coming from the production lines.The experimental analysis was carried out in an Italian cooperative firm, that process and market canned foods and frozen foods. Material flows were analysed especially considering the production of wastes. From the quantitative point of view, the hourly, daily, monthly, and annual flows of the single by-products of the studied processing cycle were determined. A dynamical simulation tool was developed to determine an optimized waste management procedure for biogas production. Design criteria was obtained for a biomass treatment to recover the organic substance for biogas production

Ambient and personal noise exposure assessment in a pasta factory

Noise pollution is one of the most important risk factors in industrial settings. This study is assessing ambient and personal noise exposure among workers of a pasta factory. Two kinds of measurements were taken; at a fixed work point in three areas and personal ones for different employees; for 8h at different times. Results for the measurements carried out at fixed sample points show that exposure times of ≤ 8h are the same. The highest noise levels are in the press and packaging areas. Worker's activity is well planned as their movements avoid staying for a long time in areas where their continuous noise exposure can exceed the most critical values. Dosimeter data can be a source of concern for the workers' health and therefore for their employers. Operators are engaged to work very close to machines; so they are subjected to levels of noise exposure different from that measured in fixed sample points. This study has further confirmed that the risk evaluation is not an exact science; as it doesn't consist only of technical and mechanical factors, but needs also to consider the factors connected to workers' interaction with the workplace

Numerical modeling of the rheological characteristic of olive paste under different conditioning treatments: Traditional malaxation, high-frequency ultrasound and microwave

Olive paste, a mixture of olive oil, vegetation water and solid particles, have a complex rheological behavior. Its viscosity (μ) cannot be considered as constant and depends on several parameters. The olive paste changes its rheological characteristics from the inlet to the outlet of the olive oil extraction line because of temperature increase and variation in fluid composition (i.e., solid-liquid m). A numerical analysis was carried out using different mathematical models to predict the apparent viscosity of olive paste as a function of the solids and olive oil volume fractions. Experimental trials were carried out processing the olive paste using different techniques: traditional malaxing (TM), the use of megasound (MS) and the use of microwaves (MW). The collected data consisted of apparent viscosity values, the related shear strain rates and the composition of the olive paste. These data were interpolated using a power law model whose parameters were determined by means of a linear regression in a bi-logarithmic scale at each step of the olive milling process. As a result of comparison with the experimental data, the different models were found to be quite effective for describing the relative viscosity behavior and the obtained solid volume fraction obtained after the three different processing methods confirms the best behavior of the MS technique. As a final consideration, the results of this work represent another step toward full comprehension of the physical characteristics of the olive paste finalizes to improve the solid-liquid separation in olive oil centrifugal decanters

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors

Energetic analysis and optimal design of a CHP plant in a frozen food processing factory through a dynamical simulation model

The proper design of cogeneration plants requires the choice of the technologies that best fits the ratio between heating and power loads. In this paper, a dynamical procedure of selecting and dimensioning a cogeneration plant, using deep and detailed energy, exergy and economic analysis of the entire production process of a frozen food production factory is proposed. The results highlight that a design method, based on a dynamic simulation, optimizes the energy efficiency of the food processing plant involved in the experimental test. Indeed, by considering the overall efficiency of the CHP + National grid system, the energy efficiency is 6% higher in the case of dynamic compared to a static design, resulting in better overall use of resources with a possible lower level of environmental impact. Moreover, the CHP plant designed with the proposed method generates electrical energy which appropriately matches that required by the process, with a surplus/deficit less than 4%, while the classic method never covers the amount required and results in a deficit greater than 20%. Finally, the annual savings of the solution derived from the dynamic method is 12% higher than that obtained with a traditional design technique. Considering the greater absolute cost of the cogeneration plant, this dynamic approach results in more profitable annual investment margins for the company

Composting of olive mill pomace, agro‐industrial sewage sludge and other residues: Process monitoring and agronomic use of the resulting composts

The viability of co‐composting of olive mill pomace added to sewage sludge with other organic residues was evaluated and the agronomic use of the final composts was investigated. Two composting piles at different carbon‐nitrogen ratios were performed, in which olive mill pomace (OMP), sewage sludge from vegetable processing (SS), fresh residues from artichoke processing residues (AR), and wheat straw (WS) were used. The two composting piles were placed inside a spe-cially built greenhouse and a turning machine pulled by a tractor was used for turning and shred-ding the organic matrix (every 6 days) during the process. The humidity and temperature of organic matrices have been monitored and controlled during the entire composting process, which lasted 90 days. The process was also monitored to evaluate the microbiological safety of the final compost. The humidity of both piles was always kept just above 50% until the end of the thermophilic phase and the maximum temperature was about 50 °C during the thermophilic phase. The carbon‐nitro-gen ratio decreased from 21.4 and 28.2, respectively (initial value at day 1 in Pile A and B), to values ranging from 12.9 to 15.1, both composts that originated from the two different piles were microbi-ologically safe. During a two‐year period, the effects of different types of compost on the main qualitative parameters of processing tomato and durum wheat was evaluated. Five fertilization treatments were evaluated for tomato and durum wheat crops: unfertilized control (TR1); compost A (TR2); compost B (TR3); ½ mineral and ½ compost A (TR4); and mineral fertilizer commonly used for the two crops (TR5). Concerning the processing tomato yield, TR5 and TR4 showed the best results (2.73 and 2.51 kg, respectively). The same trend was observed considering the marketable yield per plant. The only difference was related to the treatments that included the compost (2.32, 1.77, and 1.73 kg/plant for TR4, TR3, and TR2, respectively). As regards the qualitative parameters of to-mato, the highest average weight of the fruits was found in the TR5, TR4, and TR3 treatments (re-spectively, 73.67 g, 70.34 g, and 68.10 g). For durum wheat, only the protein component was differ-entiated between treatments. Furthermore, wheat grain yield parameters generally increased by combined application of mineral fertilizer and compost

Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images

When hyperspectral images are analyzed, a big amount of data, representing the reflectance at hundreds of wavelengths, needs to be processed. Hence, dimensionality reduction techniques are used to discard unnecessary information. In order to detect the so called “saliency”, i.e., the relevant pixels, we propose a bottom-up approach based on three main ingredients: sparse non negative matrix factorization (SNMF), spatial and spectral functions to measure the reconstruction error between the input image and the reconstructed one and a final clustering technique. We introduce novel error functions and show some useful mathematical properties. The method is validated on hyperspectral images and compared with state-of-the-art different approaches