Muhammad Hassan

Methane enhancement through co-digestion of chicken manure and thermo-oxidative cleaved wheat straw with waste activated sludge: A C/N optimization case

The present study emphasized the co-digestion of the thermal-H2O2 pretreated wheat straw (WS) and chicken manure (CM) with the waste activated sludge at four levels of C/N (35:1, 30:1, 25:1 and 20:1). All C/N compositions were found significant (P<0.05) to enhance methane generation and process stability during the anaerobic co-digestion of WS and CM. The experimental results revealed that the composition having C/N value of 20:1 was proved as optimum treatment with the methane enhancing capability of 85.11%, CODs removal efficiency of 48.55% and 66.83% VS removal as compared with the untreated WS. The other compositions having C/N of 25:1, 30:1 and 35:1 provided 75.85%, 63.04% and 59.96% enhanced methane respectively as compared with the control. Pretreatment of the WS reduced its C/N value up to 65%. Moreover, to optimize the most suitable C/N composition, the process stability of the co-digestion of WS and CM was deeply monitored.

Methane enhancement through oxidative cleavage and alkali solubilization pre-treatments for corn stover with anaerobic activated sludge

In the present study, thermo-chemical pre-treatment was adopted to evaluate methane production potential from corn stover by co-digesting it with anaerobic activated sludge. Three chemicals H2O2, Ca(OH)2 and NaOH were selected with two levels of concentration. All thermo-chemical pre-treatments were found significant (P<0.05) to enhance lignocellulosic digestibility and methane production. The results indicated that the methane yield by H2O2-1, H2O2-2, and NaOH-2 treated corn stover were 293.52, 310.50 and 279.42ml/g.VS which were 57.18%, 66.27% and 49.63% higher than the untreated corn stover respectively. In the previous studies pre-treatment time was reported in days but our method had reduced it to about one hour. H2O2-2 and NaOH-2 treatments remained prominent to increase lignocellulosic degradation vigorously up to 45% and 42% respectively. Process biochemistry during the anaerobic digestion process was taken into consideration to optimize the most feasible thermo-chemical pre-treatment for corn stover.

Batch and semi-continuous anaerobic co-digestion of goose manure with alkali solubilized wheat straw: A case of carbon to nitrogen ratio and organic loading rate regression optimization

The present study focused on carbon to nitrogen ratio (C/N) and organic loading rate (OLR) optimization of goose manure (GM) and wheat straw (WS). Dealing the anaerobic digestion of poultry manure on industrial scale; the question of optimum C/N (mixing ratio) and OLR (daily feeding concentration) have significant importance still lack in literature. Therefore, Batch and CSTR co-digestion experiments of the GM and WS were carried out at mesophilic condition. The alkali (NaOH) solubilization pretreatment for the WS had greatly enhanced its anaerobic digestibility. The highest methane production was evaluated between the C/N of 20-30 during Batch experimentation while for CSTRs; the second applied OLR of (3g.VS/L.d) was proved as the optimum with maximum methane production capability of 254.65ml/g.VS for reactor B at C/N of 25. The C/N and OLR regression optimization models were developed for their commercial scale usefulness.

Tool path optimal design for slow tool servo turning of complex optical surface

In order to increase the machining accuracy of slow tool servo turning of complex optical surface, the optimal design for tool path was studied. A comprehensive tool path generation strategy was proposed to optimize the tool path for machining complex surfaces. A new algorithm was designed for tool nose radius compensation which had less calculation error. Hermite segment interpolation was analyzed based on integrated multi-axes controller, and a new interpolation method referred to as triangle rotary method was put forward and was compared with the area method and three-point method. The machining simulation indicated that the triangle rotary method was significant in error reduction. The interpolation error of toric surface was reduced to 0.0015u2009µm from 0.06u2009µm and sinusoidal array surface’s interpolation error decreases to 0.37u2009µm from 1.5u2009µm. Finally, a toric surface was machined using optimum tool path generation method to evaluate the proposed tool path generation method.

Alkaline Pretreatment of Sugarcane Bagasse and Filter Mud Codigested to Improve Biomethane Production

To enhance the codigestion of degradation and improve biomethane production potential, sugarcane bagasse and filter mud were pretreated by sodium hydroxide NaOH 1u2009N at 100°C for 15, 30, and 45 minutes, respectively. Biomethane generation from 1-liter batch reactor was studied at mesophilic temperature (37 ± 1)°C, solid concentrations of 6%, and five levels of mixing proportion with and without pretreatment. The results demonstrate that codigestion of filter mud with bagasse produces more biomethane than fermentation of filter mud as single substrate; even codigested substrate composition presented a better balance of nutrients (C/N ratio of 24.70) when codigestion ratio between filter mud and bagasse was 25u2009:u200975 in comparison to filter mud as single substrate (C/N ratio 9.68). All the pretreatments tested led to solubilization of the organic matter, with a maximum lignin reduction of 86.27% and cumulative yield of biomethane (195.8u2009mL·gVS−1, digestion of pretreated bagasse as single substrate) obtained after 45 minutes of cooking by NaOH 1u2009N at 100°C. Under this pretreatment condition, significant increase in cumulative methane yield was observed (126.2u2009mL·gVS−1) at codigestion ratio of 25u2009:u200975 between filter mud and bagasse by increase of 81.20% from untreated composition.

A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model.

Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.

Modeling and Multiresponse Optimization for Anaerobic Codigestion of Oil Refinery Wastewater and Chicken Manure by Using Artificial Neural Network and the Taguchi Method

To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L9 (34) Taguchis orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36°C temperature, 30u2009min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76u2009mL/gVS, 151.95u2009mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained.