The effect of mixed-enzyme addition in anaerobic digestion on methane yield of dairy cattle manure

This study investigates the effect of applying a mixture of enzymes (ME) to dairy cattle manure (DCM) as substrate in anaerobic digestion (AD). The aims of this study were to evaluate different methods of ME application to DCM at different temperatures and to investigate the effect of adding ME during the pre-treatment of the solid fractions of dairy cattle manure (SFDCM). The results showed that there was no positive effect of direct ME addition to substrate at either mesophilic (35 C) or thermophilic (50 ◦ C) process temperatures, but there was a significant 4.44% increase in methane yield when DCM, which had been incubated with ME addition at 50 ◦ C for three days, was fed to a digester when compared to a control digester operating at the same retention time. Methane production was detected during the pre-treatment incubation, and the total sum methane yield during pre-treatment and digestion was found to be 8.33% higher than in the control. The addition of ME to the SFDCM in a pre-incubation stage of 20 h at 35 ◦ C gave a significant increase in methane yield by 4.15% in a digester treating a mixed substrate (30% liquid fractions DCM and 70% enzyme-treated SFDCM) when compared with the control digester treating a similar mixed substrate with inactivated enzyme addition. The results indicate that direct physical contact of enzyme molecules and organic material in DCM prior to AD, without the intervention of extracellular enzymes from the indigenous microorganism population, was needed in order to increase methane yields. Keywords: biogas; mixed enzymes; pre-treatment; incubation; manur

Motivating dualities

There exists a common view that for theories related by a `duality', dual models typically may be taken \emph{ab initio} to represent the same physical state of affairs, i.e. to correspond to the same possible world. We question this view, by drawing a parallel with the distinction between `interpretational' and `motivational' approaches to symmetries

Motivating dualities

There exists a common view that for theories related by a `duality', dual models typically may be taken \emph{ab initio} to represent the same physical state of affairs, i.e. to correspond to the same possible world. We question this view, by drawing a parallel with the distinction between `interpretational' and `motivational' approaches to symmetries

A Case Study On Empowering A Non-Profit Organization To Better Help People With Disabilities Through M-Health

Local non-profit organizations are constrained in developing efficient methods for helping people with disabilities confined at their own homes. The cost of labor of physically serving such people is a continued issue at the organizations. This case study explores an entrepreneurial focus on best-in-class applications of m-Health devices for improving methods of home medication support furnished by a leading metropolitan non-profit organization. This study explores further the potential of hosted infrastructure-as-a-service (IaaS) m-Health remote monitoring systems technology. The findings of this study can benefit non-profit organizations considering economic entrepreneurial innovation in interactive managed care technology

OnTrack: Reflecting on domain specific formal methods for railway designs

OnTrack is a tool that supports workflows for railway verification that has been implemented using model driven engineering frameworks. Starting with graphical scheme plans and finishing with automatically generated formal models set-up for verification, OnTrack allows railway engineers to interact with verification procedures through encapsulating formal methods. OnTrack is grounded on a domain specification language (DSL) capturing scheme plans and supports generation of various formal models using model transformations. In this paper, we detail the role model driven engineering takes within OnTrack and reflect on the use of model driven engineering concepts for developing domain specific formal methods toolsets

On modelling and verifying railway interlockings: Tracking train lengths

The safety analysis of interlocking railway systems involves verifying freedom from collision, derailment and run-through (that is, trains rolling over wrongly-set points). Typically, various unrealistic assumptions are made when modelling trains within networks in order to facilitate their analyses. In particular, trains are invariably assumed to be shorter than track segments; and generally only a very few trains are allowed to be introduced into the network under consideration. In this paper we propose modelling methodologies which elegantly dismiss these assumptions. We first provide a framework for modelling arbitrarily many trains of arbitrary length in a network; and then we demonstrate that it is enough with our modelling approach to consider only two trains when verifying safety conditions. That is, if a safety violation appears in the original model with any number of trains of any and varying lengths, then a violation will be exposed in the simpler model with only two trains. Importantly, our modelling framework has been developed alongside - and in conjunction with - railway engineers. It is vital that they can validate the models and verification conditions, and - in the case of design errors - obtain comprehensible feedback. We demonstrate our modelling and abstraction techniques on two simple interlocking systems proposed by our industrial partner. As our formalization is, by design, near to their way of thinking, they are comfortable with it and trust it

Effects of high-temperature isochoric pre-treatment on the methane yields of cattle, pig and chicken manure

Cattle manure, dewatered pig manure and chicken manure were pre-treated in a high-temperature reactor under isochoric conditions for 15 min at temperatures between 100 and 225 ◦ C with 25 ◦ C intervals to study the effect on their methane yield. After 27 days of batch incubation, cattle manure showed a significant improvement in its biochemical methane potential (BMP) of 13% at 175 ◦ C and 21% at 200 ◦ C. Pig manure showed improvements at temperatures of 125 C and above, with a maximum 29% increase in yield at 200 ◦ C. The BMP of chicken manure was reduced by 18% at 225 C, but at lower temperatures there were no significant changes. It was found that this method of pre-treatment could be feasible if sufficient surplus energy was available or if the energy used in the pre-treatment could be recovered. Keywords: BMP; manure; biogas; thermal; pre-treatment; energy requirement

First Results from the Large Area Lyman Alpha Survey

We report on a new survey for z=4.5 Lyman alpha sources, the Large Area Lyman Alpha (LALA) survey. Our survey achieves an unprecedented combination of volume and sensitivity by using narrow-band filters on the new 8192x8192 pixel CCD Mosaic Camera at the 4 meter Mayall telescope of Kitt Peak National Observatory. Well-detected sources with flux and equivalent width matching known high redshift Lyman alpha galaxies (i.e., observed equivalent width above 80 Angstroms and line+continuum flux between 2.6e-17 and 5.2e-17 erg/cm^2/sec in an 80 Angstrom filter) have an observed surface density corresponding to 11000 +- 700 per square degree per unit redshift at z=4.5. Spatial variation in this surface density is apparent on comparison between counts in 6561 and 6730 Angstrom filters. Early spectroscopic followup results from the Keck telescope included three sources meeting our criteria for good Lyman alpha candidates. Of these, one is confirmed as a z=4.52 source, while another remains consistent with either z=4.55 or z=0.81. We infer that 30 to 50% of our good candidates are bona fide Lyman alpha emitters, implying a net density of about 4000 Lyman alpha galaxies per square degree per unit redshift.Comment: 10 pages, 2 figures (3 .ps files), uses AASTeX 4. Submitted to The Astrophysical Journal Letter

Neural Network-Based Sensor Validation for Turboshaft Engines

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures