Andrzej Lewicki

Co-composting of poultry manure mixtures amended with biochar – The effect of biochar on temperature and C-CO2 emission

Biochar as an amendment could have an impact on composting dynamics. This study investigated the effect of the addition of biochar (B) to poultry manure (P) mixed with wheat straw (S) (i.e. P:S, P:S+5%B and P:S+10%B) on temperature and carbon dioxide emission. For temperature studies a modified equation for net degree-hour parameter DHnet (°Chday(-1)) was proposed. The modified equation takes into account ambient temperature. The highest daily temperatures DHnet were observed on day 2 and the mixture with the highest addition of biochar (P:S+10%) reached the max temperature. The period of thermophilic temperatures (40°C>) was shorter for mixtures amended with biochar. The addition of biochar increased C-CO2 emission and the total C-CO2 emission were higher about 6.9% and 7.4% for P:S+5%B and P:S+10%B, respectively.

Possibilities of neural image analysis implementation in monitoring of microalgae production as a substrate for biogas plant

The paper presents the possibilities of neural image analysis of microalgae content in the large-scale algae production for usage as a biomass. With the growing conflict between the culture produced both for feed and energetic purpose in Europe, the algae production seems to be very efficient way to produce the huge amount of biomass outside of conventional agronomy. However, for stable microalgae production the key point for culture management is the rapid estimation of algae population and assessment of its developmental stage. In traditional way the microalgae content is usually checked by the long microscopic analyses which cannot be used in large-scale industrial cultivation. Moreover, highly specialized personnel is required for algal determinations. So the main aim of this study is to estimate the possibility of usage of automatic image analysis of microalgae content made by artificial neural network. The preliminary results show that the selection of artificial neural network topology for the microalgae identification allowed for the selection and choice of teaching variables obtained by studying the image analysis. The selected neural model on the basis of data from computer image analysis allows to carry out the operations of algae identification and counting. On the basis of the obtained results of preliminary tests it is possible to count the algae on the photos. Additional information on their size and color allows to unlimited categorization.

Biochar to reduce ammonia emissions in gaseous and liquid phase during composting of poultry manure with wheat straw

Composting of poultry manure which is high in N and dense in structure can cause several problems including significant N losses in the form of NH3 through volatilization. Biochar due to its recalcitrance and sorption properties can be used in composting as a bulking agent and/or amendment. The addition of a bulking agent to high moisture raw materials can assure optimal moisture content and enough air-filled porosity but not necessarily the C/N ratio. Therefore, amendment of low C/N composting mixtures with biochar at low rates can have a positive effect on composting dynamics. This work aimed at evaluating the effect of selected doses of wood derived biochar amendment (0%, 5% and 10%, wet weight) to poultry manure (P) mixed with wheat straw (S) (in the ratio of 1:0.4 on wet weight) on the total ammonia emissions (including gaseous emissions of ammonia and liquid emissions of ammonium in the collected condensate and leachate) during composting. The process was performed in 165L laboratory scale composting reactors for 42days. The addition of 5% and 10% of biochar reduced gaseous ammonia emission by 30% and 44%, respectively. According to the obtained results, the measure of emission through the condensate would be necessary to assess the impact of the total ammonia emission during the composting process.

Quality assessment of microwave-vacuum dried material with the use of computer image analysis and neural model

The farming area for vegetables in Poland is constantly changed and modified. Each year the cultivation structure of particular vegetables is different. However, it is the cultivation of carrots that plays a significant role among vegetables. According to the Main Statistical Office (GUS), in 2012 carrot held second position among the cultivated root vegetables, and it was estimated at 835 thousand tons. In the world we are perceived as the leading producer of carrot, due to the fourth place in the ranking of global producers. Poland is the largest producer of this vegetable in the EU [1]. It is also noteworthy, that the demand for dried vegetables is still increasing. This tendency affects the development of drying industry in our country, contributing to utilization of the product surplus. Dried vegetables are used increasingly often in various sectors of food products industry, due to high nutrition value, as well as to changing alimentary preferences of consumers [2-3]. Dried carrot plays a crucial role among dried vegetables, because of its wide scope of use and high nutrition value. It contains a lot of carotene and sugar present in the form of crystals. Carrot also undergoes many different drying processes, which makes it difficult to perform a reliable quality assessment and classification of this dried material. One of many qualitative properties of dried carrot, having important influence on a positive or negative result of the quality assessment, is color and shape. The aim of the research project was to develop a method for the analysis of microwave-vacuum dried carrot images, and its application for the classification of individual fractions in the sample studied for quality assessment. During the research digital photographs of dried carrot were taken, which constituted the basis for assessment performed by a dedicated computer programme developed as a part of the research. Consequently, using a neural model, the dried material was classified [4-6].

Computer image analysis in obtaining characteristics of images: greenhouse tomatoes in the process of generating learning sets of artificial neural networks

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

Organoleptic damage classification of potatoes with the use of image analysis in production process

In the agro-food sector security it is required the safety of a healthy food. Therefore, the farms are inspected by the quality standards of production in all sectors of production. Farms must meet the requirements dictated by the legal regulations in force in the European Union. Currently, manufacturers are seeking to make their food products have become unbeatable. This gives you the chance to form their own brand on the market. In addition, they use technologies that can increase the scale of production. Moreover, in the manufacturing process they tend to maintain a high level of quality of their products. Potatoes may be included in this group of agricultural products. Potatoes have become one of the major and popular edible plants. Globally, potatoes are used for consumption at 60%, Poland 40%. This is due to primarily advantages, consumer and nutritional qualities. Potatoes are easy to digest. Medium sized potato bigger than 60 mm in diameter contains only about 60 calories and very little fat. Moreover, it is the source of many vitamins such as vitamin C, vitamin B1, vitamin B2, vitamin E, etc. [1]. The parameters of quality consumer form, called organoleptic sensory properties, are evaluated by means of sensory organs by using the point method. The most important are: flavor, flesh color, darkening of the tuber flesh when raw and after cooking. In the production process it is important to adequate, relevant and accurate preparing potatoes for use and sale. Evaluation of the quality of potatoes is determined on the basis of organoleptic quality standards for potatoes. Therefore, there is a need to automate this process. To do this, use the appropriate tools, image analysis and classification models using artificial neural networks that will help assess the quality of potatoes [2, 3, 4].

Specialised Internet Tool for Biogas Plant Modelling and Marked Analysing

The paper presents the internet tool for decision support and data acquisition for Polish biogas market. This system helps investors to plan the size and potential power of biogas plant with usage of selected, own substrates. Because of open access via internet, the BiogasWebPlanner® allows to create one of the biggest databases about future Polish biogas market.

Neural image analysis in the process of quality assessment: domestic pig oocytes

The questions related to quality classification of animal oocytes are explored by numerous scientific and research centres. This research is important, particularly in the context of improving the breeding value of farm animals. The methods leading to the stimulation of normal development of a larger number of fertilised animal oocytes in extracorporeal conditions are of special importance. Growing interest in the techniques of supported reproduction resulted in searching for new, increasingly effective methods for quality assessment of mammalian gametes and embryos. Progress in the production of in vitro animal embryos in fact depends on proper classification of obtained oocytes. The aim of this paper was the development of an original method for quality assessment of oocytes, performed on the basis of their graphical presentation in the form of microscopic digital images. The classification process was implemented on the basis of the information coded in the form of microphotographic pictures of the oocytes of domestic pig, using the modern methods of neural image analysis.

The recognition of potato varieties using of neural image analysis method

The aim of this paper was to extract the representative features and generate an appropriate neural model for classification of varieties of edible potato. Potatoes of variety the Vineta and the Denar were the empirical object of this thesis. The main concept of the project was to develop and prepare an image database using the computer image analysis software. The choice of appropriate neural model the one which will have the greatest abilities to identify the selected variety. The aim of this project is ultimately to conduct assistance and accelerate work of the expert, who classifies and keeps different varieties of potatoes in heaps.

The Control of Air Humidity and Temperature in Relationship with a Biowaste Composting Process

Biowaste composting is one of the main technologies of waste management in Poland. Composting process run is influenced by many parameters which can accelerate or slow down the phenomena. However, there is no information about the influence of humidity of the air pumped to composted mass on process run. Thus, the aim of this paper is to analyze the influence of air humidity on parameters of composting process. The results have showed that there is no significant influence of the air humidity on a dynamics of composting process. Even in case when the difference in air humidity exceeded 60%, the temperature remained similar. The research proved utility of new kind of temperature sensors. Temperature analysis in the whole volume of composting chamber allows to control composting process in much more effective way.