Bioenergy
M. Kamali; R. Abdi; A. Rohani; Sh. Abdollahpour; S. Ebrahimi
Abstract
IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance ...
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IntroductionSince anaerobic digestion leads to the recovery of energy and nutrients from waste, it is considered the most sustainable method for treating the organic fraction of municipal solid wastes.However, due to the long solid retention time in the anaerobic digestion process, the low performance of the process in biogas production as well as the uncertainty related to the safety of digested materials for utilizing in agriculture, applying different pretreatments is recommended.Thermal pretreatment is one of the most common pretreatment methods and has been used successfully on an industrial scale. Very little research, nevertheless, has been done on the effects of different temperatures and durations of thermal pretreatment on the enhancement of anaerobic digestion of the organic fraction of municipal solid wastes (OFMSW). The main effect of thermal pretreatment is the rapturing cell membrane and dissolving organic components. Thermal pretreatment at temperatures above 170 °C may result in the formation of chemical bonds that lead to particle agglomeration and can cause the loss of volatile organic components and thus reduce the potential for methane production from highly biodegradable organic waste. Therefore, since thermal pretreatment at temperatures above 100 °C and high pressure requires more energy and more sophisticated equipment, thermal pretreatment of organic materials at low temperatures has recently attracted more attention. According to the researchers, thermal pretreatment at temperatures below 100 °C did not lead to the decomposition of complex molecules but the destruction of large molecule clots.The main purpose of this study was to find the optimal levels of pretreatment temperature and time and the most appropriate concentration of digestible materials to achieve maximum biogas production using a combination of the Box Behnken Response Surface Method to find the objective function followed by optimizing these variables by Genetic Algorithm.Materials and MethodsIn this study, the synthetic organic fraction of municipal solid waste was prepared similar to the organic waste composition of Karaj compost plant. The digestate from the anaerobic digester available in the Material and Energy Research Institute was used as an inoculum for the digestion process. Some characteristics of the raw materials that are effective in anaerobic digestion including the moisture content, total solids, volatile solids of organic waste, and the inoculum were measured. Experimental digesters were set up according to the model used by MC Leod. After size reduction and homogenization, the synthetic organic wastes were subjected to thermal pretreatment (70, 90, 110 °C) at specific times (30, 90, 150 min).The Response Surface methodology has been used in the design of experiments and process optimization. In this study, three operational parameters including pretreatment temperature, pretreatment time, and concentration of organic material (8, 12, and 16%) were analyzed. After extracting the model for biogas efficiency based on the relevant variables, the levels of these variables that maximize biogas production were determined using a Genetic Algorithm.Results and DiscussionThe Reduced Quadratic model, was used to predict the amount of biogas production. The value of the correlation coefficient between the two sets of real and predicted data was more than 0.95. The results suggested that pretreatment time followed by the pretreatment temperature had the greatest contribution (50.86% and 44.81%, respectively) to biogas production. Changes in the organic matter concentration, on the other hand, did not have a significant effect (p ˂ 0.01) on digestion enhancement (1.63%) but were statistically significant at p ˂ 0.10.The response surface diagram showed that the increase in pretreatment time first led to a rise and then a fall in biogas production. The decline in biogas production seemed set to continue with pretreatment time. Meanwhile, the increase in pretreatment temperature from 70 °C to 110 °C first contributed to higher biogas production and then the decrease in gas production occurred. The reason for this fall was probably the browning and Maillard reaction.The regression model was applied as the objective function for variables optimization using the Genetic Algorithm method. Based on the results of this algorithm, the optimal thermal pretreatment for biogas production was determined at 95 °C for 104 minutes and at the concentration of 12%. The expected amount of biogas production by applying the optimal pretreatment conditions was 445 mL-g-1 VS.ConclusionIn this study, the variables including thermal treatment temperature and time as well as the concentration of organic waste to be anaerobically digested were optimized to achieve the highest biogas production from anaerobic digestion.Statistical analysis of the results revealed that the application of thermal pretreatment increased biogas production considerably. According to the regression model, the contribution of pretreatment time and temperature to biogas production was significant (50.86% and 44.81% respectively). In stark contrast, varying substrate concentrations in the range of 8 to 16% had a smaller effect (1.63%) on biogas production. The results of this study also showed that the best pretreatment temperature and time were 95 °C and 104 minutes, respectively, at a concentration of 12% by generating 445 mL-g-1 VS biogas which is 31.17% higher than the biogas yield from anaerobic digestion of untreated organic wastes at this concentration.
Z. Ramedani; R. Abdi; M. Omid; M. A. Maysami
Abstract
Introduction Life cycle assessment of food products is an appropriate method to understand the energy consumption and production of environmental burdens. Dairy production process has considerable effect on climate change in various ways, and the scale of these effects depends on the practices of dairy ...
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Introduction Life cycle assessment of food products is an appropriate method to understand the energy consumption and production of environmental burdens. Dairy production process has considerable effect on climate change in various ways, and the scale of these effects depends on the practices of dairy industry, dairy farmers and feed growers. This study examined the life cycle of production of dairy products in Kermanshah city. For this purpose, the whole life was divided in two sections: production of raw milk in dairy farm and dairy products in dairy industry. In each section the energy consumption patterns and environmental burdens were evaluated. Based on the results, the consumed energy in dairy farm was 6286.29 MJ for amount of produced milk in month. Also animal feed was the greatest energy consumer with the value of 45.12% that the maximum amount of this value was for concentrate. The minimum consumption of energy was for the machinery with 0.92 MJ in a month. Results of life cycle assessment of dairy products showed that in dairy industry raw milk input causes most of impact categories especially land use, carcinogens and acidification. In dairy farms, concentrate was effective more than 90% in production of impact categories included: land use and carcinogens. Using digesters for production biogas and solar water heaters in dairy farm can decrease fossil recourses. Materials and Methods Based on ISO 14044, standards provide an overview of the steps of an LCA: (1) Goal and Scope Definition; (2) Life Cycle Inventory Analysis; (3) Life Cycle Impact Assessment; and (4) Interpretation (ISO, 2006). In this study there were two sub-systems in the production line: dairy farm sub-system (1) and dairy factory sub-system (2). In the sub-system related to the dairy farm, the main product was milk. Determination of inputs and outputs in each sub-system, energy consumption, transportation and emissions to air and water as well as waste treatment are the requirements of LCI. However each of them has several components. These components are different in both sub-systems. All the detailed data about energy equivalent in dairy farm is shown in Table 1. More detailed data about inventories description of two sub-systems are shown in Tables 3 and 4. The SimaPro 7.3.2 was used for analyzing the collected data for calculating environmental burdens (Pré Consultants, 2012). Results and Discussion Based on the developed models with SimaPro software for dairy products in the factory, various emissions were generated including emissions into the air, soil and water. The most prevalent emissions are summarized in Table 7. In warm season about half of the milk is processed into drinking yoghurt. Since water is one half of the component of this product so more amount of drinking yoghurt can be achieved with lower energy consumption (about 50%). Furthermore, these results indicated that the magnitude of fossil fuels was much greater than all others. It was followed by land use and respiratory inorganics. The most amount of the consumption of the fossil fuels was the production of energy requirements for heating systems at boilers and tractors in dairy factory and farm, respectively. Also the transportation of raw milk to the dairy industry was another source of the pollution. Also the energy consumption pattern in the dairy farm revealed that the concentrate have high contribution in energy consumption. Conclusion Results of the energy consumption pattern showed that the animal feed was the greatest energy consumer with value of 45.12% and followed by electricity (36%). Energy consumption index for the fossil fuel was calculated about 3.8 that is higher than the global index. Production of raw milk in dairy farm is responsible in the production of impact categories especially land use, carcinogenic and acidification with contribution of 97.6%, 78%, and 63%, respectively. Also the amount of CO2-eq was estimated 2.71 kg for the production of 1kg ECM in cold seasons.
Modeling
J. Taghinazhad; R. Abdi; M. Adl
Abstract
Introduction Anaerobic digestion (AD) is a process of breaking down organic matter, such as manure, in the absence of oxygen by concerted action of various groups of anaerobic bacteria. The AD process generates biogas, an important renewable energy source that is composed mostly of methane (CH4), and ...
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Introduction Anaerobic digestion (AD) is a process of breaking down organic matter, such as manure, in the absence of oxygen by concerted action of various groups of anaerobic bacteria. The AD process generates biogas, an important renewable energy source that is composed mostly of methane (CH4), and carbon dioxide (CO2) which can be used as an energy source. Biogas originates from biogenic material and is therefore a type of biofuel. Enhancement of biogas production from cattle dung or animal wastes by co-digesting with crop residues like sugarcane stalk, maize stalks, rice straw, cotton stalks, wheat straw, water hyacinth, onion waste and oil palm fronds as well as with liquid waste effluent such as palm oil mill effluent. Nevertheless, the search for cost effective and environmentally friendly methods of enhancing biogas generation (i.e. biogas yield) still needs to be further investigated. Many workers have studied the reaction kinetics of biogas production and developed kinetic models for the anaerobic digestion process. Objective of this study is to investigate the effect of biological additive using of organic loading rate (OLR) in biogas production from cow dung. In addition, cumulative biogas production was simulated using logistic growth model, and modified Gompertz models, respectively. Materials and Methods The study was performed in 2015-2016 at the agricultural research center of Ardabil Province, Moghan (39.39 °N, 48.88° E). Fresh cow manure used for this research was collected from the research farm of the Institute for Animal Breeding and Animal Husbandry, Moghan. It was kept in 30 l containers at ambient temperature until fed to the reactors. In this study, experiments were conducted to investigate the biogas production from anaerobic digestion of cow manure (CM) with effect of organic loading rate (OLR) at mesophilic temperature (35°C±2) in a long time experiment with completely stirred tank reactor (CSTR) under semi continuously feeding. The complete-mix, pilot-scale digester with working volume of 180 l operated at different organic feeding rates of 2 and 3 kg VS. (m-3.d-1). the biogas produced was measured daily by water displacement method and its composition was measured by gas chromatograph. Total solids (TS), volatile solids (VS), pH and etc. were determined according to the APHA Standard Methods. The biogas production kinetics for the description and evaluation of methanogens was carried out by fitting the experimental data of biogas production to various kinetic equations. In addition, Specific cumulative biogas production was simulated using logistic kinetic model exponential Rise to Maximum and modified Gompertz kinetic model. Results and Discussion The experimental protocol was defined to examine the effect of the change in the organic loading rate on the efficiency of biogas production and to report on its steady-state performance. The biogas produced had methane composition of 58- 62% and biogas production efficiency 0.204 and 0.242 m3 biogas (kg VS input) for 2 and 3 kg VS.(m-3.d-1), respectively. The reactor showed stable performance with VS reduction of around 64 and 53% during loading rate of 2 and 3 kg VS.(m-3.d-1), respectively. Other studies showed similar results. Modified Gompertz and logistic plot equation was employed to model the biogas production at different organic feeding rates. The equation gave a good approximation of the biogas yield potential (P) and correlation coefficient (R2) over 0.99. Conclusion The performance of anaerobic digestion of cow dung for biogas production using a completely stirred tank reactor was successfully examined with two different organic loading rate (OLR) under semi continuously feeding regime in mesophilic temperature range at (35°C±2). The methane content of 58- 62% and actual biogas yield of 0.204 and 0.242 m3 biogas.(kg VS input-1) were observed for 2 and 3 kg VS. (m-3.d-1), respectively. The modeling results suggested Modified Gompertz plot and Logistic growth plot both had higher correlation for simulating cumulative biogas production. Therefore, arising from the increasing environmental concern and prevailing wastes management crises, optimizing biogas production by 2 kg VS. (m-3.d-1) represents a viable and sustainable energy option.
M. Safari; R. Abdi
Abstract
Introduction
Seventy million tons of agricultural crops are produced from 18 million hectares of agricultural lands in Iran every year. Since 80% of the crops (wt. basis) ends up as residues, therefore, about 50 million tons of crop residues are generated annually the majority of which is burnt on field ...
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Introduction
Seventy million tons of agricultural crops are produced from 18 million hectares of agricultural lands in Iran every year. Since 80% of the crops (wt. basis) ends up as residues, therefore, about 50 million tons of crop residues are generated annually the majority of which is burnt on field leading to vast emissions of greenhouse gases (GHG) due to the incomplete combustion process. These residues could potentially be transformed into heat energy directly by adopting a burning process or indirectly by first transforming them into secondary fuel as hydrogen, bio-methane, methanol or ethanol.
Materials and Methods
The present study was conducted using, wheat and rapeseed straws dried at ambient temperature co-digested with fresh cow dung while the total solid content and detention time were kept constant. To conduct the Anaerobic Digestion (AD) experiments, cylinder reactors (13 L) were constructed and placed in a water bath equipped with a heater and sensor to maintain the temperature at 35±2 oC. The biogas produced in the digester was investigated by measuring the displacement of the water in a measuring tube connected to the reactor. Gas samples were obtained from the sampling port and were analyzed gas chromatograph. The temperature for detector, injector and oven were 170, 110 and 50 oC respectively. Before the test, the first CH4 and CO2 net gases, peaks corresponding percentage was determined with respect to the retention time of the area. Then sample was compared with standard gas and samples gas percentage was determined. The residues were mechanically pretreated using a mill in order to increase the availability of the biomass to enzymes. After the pre-treatment, the material (<2 mm) was mixed with a different proportion of fresh cow dung, Initial Total Solids (TS) content in the reactor was adjusted at 9%. Factors such as PH, Volatile Solids (VS) were determined by the standard method.
Results and Discussion
A decrease in the process pH was observed in the first few days of the digestion and this is due to high volatile fatty acid (VFA) formation. These results were compatible with sanaee moghadam et al. (2013). The results obtained showed that, the highest rate of VS reduction belonged to rapeseed residues at 52.22%.The lowest rate of VS reduction attributed to wheat residues at 36.79%. The rapeseed residues with 311.45 Lit.kg-1 VS had the highest accumulated methane followed by wheat straw with 167.69.28 L.Kg-1 VS in probability level of 5%. The average percentages of methane production for rapeseed straw and wheat straw during the 140 days experiment under mesophilic condition were 66% and 55%, respectively. Production of methane had delay and started after 46th day. Much reason may be possible. Inoculums used in this study were only fresh cattle dung. The mixture of fresh cattle dung and effluent of anaerobic digester or fresh rumen fluid may be decrease retention time and increase biogas production. According results of Budiyono the rumen fluid inoculated to biodigester significantly affected the biogas production. Rumen fluid inoculums caused biogas production rate and efficiency increase more than two times in compare to manure substrate without rumen fluid inoculums (Budyono et al., 2010). The other reason was pretreatment. This study applied just mechanical pretreatment. According to Cecilia studies, different pretreatment combined with mechanical pretreatment decrease retention time and increase biogas production efficiency (Cecilia et al, 2013). However, Zhang et al. claimed that it is hard to say which method is the best because each has its own strong point and weak point. Yet, until now, none of the pretreatment technologies has found a real breakthrough.
Conclusions
According to this study, rapeseed residues had the highest level of methane production in comparison with wheat residues. The rapeseed residues combine with cattle dung had suitable potential to methane production. The 140 days, Biomaethane Potential (BMP) of rapeseed residues combine with cattle manure had 311. 45 Lit/kg vs. add. Moreover, it had high percentage of VS content reduction (52.22%). The high retention time was observed (140 day). One reason was lack of suitable inoculums and pretreatment. Furthermore, the lingo-cellulose nature of the crop residues, lower will be the biodegrade ability. Furthermore, the anaerobic co-digestion of rapeseed straw with cattle manure is feasible for production of methane.
M. Taki; Y. Ajabshirchi; R. Abdi; M. Akbarpour
Abstract
In this research energy efficiency for greenhouse cucumber production in Shahreza township located in Esfahan province using data envelopment analysis (DEA) technique was studied. In this study, data were obtained from 25 randomize active vegetable greenhouses from 60 greenhouses in Shahreza township ...
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In this research energy efficiency for greenhouse cucumber production in Shahreza township located in Esfahan province using data envelopment analysis (DEA) technique was studied. In this study, data were obtained from 25 randomize active vegetable greenhouses from 60 greenhouses in Shahreza township and villages environs. The results showed that the highest and lowest consumed energy were related to fuel and water inputs with 47% and 1.2% respectively. The results of data envelopment analysis showed in CCR and BCC models 24% and 36% of farmers were efficient and the others were inefficient. Mean technical efficiency, net technical efficiency and scale efficiency were calculated as 90.37, 95.09 94.6 respectively. Also technical efficiency of inefficiency units in CCR model was 87% that shows13% of total energy input could be saved with upgrade efficiency in these units. In this research, total saved and unsaved energy related to fuel consumption.
Y. Ajabshirchi; M. Taki; R. Abdi; A. Ghobadifar; I. Ranjbar
Abstract
In this research energy efficiency for dry wheat production in three levels including 0.1 up to2, 2.1 up to 5 and over 5.1 hectares for the farming year 2008-2009 in Silakhor plain located in Borujerd and Dorud divisions of Lorestan province was studied using data envelopment analysis (DEA) technique. ...
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In this research energy efficiency for dry wheat production in three levels including 0.1 up to2, 2.1 up to 5 and over 5.1 hectares for the farming year 2008-2009 in Silakhor plain located in Borujerd and Dorud divisions of Lorestan province was studied using data envelopment analysis (DEA) technique. The results showed that the input energy for seed, fertilizer and pesticides had the highest levels of energy consumption and the share of that in each studied level were 63.63, 56 and 54.07 percent respectively. The results of data envelopment analysis showed that the average of energy efficiency levels were 82, 78 and 68 percent, respectively. First level, that consumes more input energy than other two studied levels, had highest energy efficiency, because in this level output yield were more than other levels. Technical efficiency of inefficiency units in CRS model in three levels is 79%, 77% and 66% respectively. This issue indicates that 21, 23 and 34 of total energy input could be saved with upgrade efficiency in these units. All wrong using and also all share of total saved energy in three levels related to grain, fertilizer and pesticides and then fuel consumption.
