Development and assessment of a fast pyrolysis reactor for bio-oil, syngas and bio-char production from biomass residues

Design, development and assessment of a Fluidized Bed Reactor (FBR) is a very complex process, where enormous empirical correlations; charts and graphs; lot of parameters, assumptions, unit operations are involved, straight forward design equations and design data are limited, and generally the operation of the system requires many adjustments. The improved design of FBR with high coefficient of performance (COP), low energy consumption, high yield and environmentally friendly (low emission) is the target. The scope of the study is to design and fabrication of a lab scale fluidized bed fast pyrolysis system with throughput capacity of 1 kg of dry biomass per hour which includes a bubbling fluidized bed reactor, 2 cyclone separators in series, 4 condensers in series operating between temperatures of 600-300; 300-200; 200-125 and 125-40 ̊C to selectively condense alkanes, phenols, aromatics, indene, methyl-indene, benzene, toluene, methyl–naphthalene, esters, acids, alcohols, ketones; 2 heaters (1 pre-and 1 primary), an auger feeder with hopper and controller, blowers and rig structure. A 3-D simulation was performed to facilitate the mounting of different unit operations, instruments and control panels with sufficient maintenance and manoeuvring accessibilities yet compact structure with low structural footprints. The rig is having the dimensions of 2204X2750X1100mm (L x H x W) and suitable for batch operation to produce about 650 gm bio-oil, 150 gm non-condensable and 200 gm bio-char from 1kg of dry biomass pyrolysis. The rig is manually operated, however the data acquisition and logging systems are digital and has provision of scrubbing exhaust gas, and an online analyser has been installed to measure and monitor lower hydrocarbons including hydrogen concentrations and Lower Explosive Limit (LEL) in the exhaust gas. Four types of biomass such as Empty fruit bunch (EFB), Urban tree shavings (UTS), Saw dust Broga (SDB) and Saw dust Semenyih (SDS) were pre-treated with aqueous acidic (H2SO4) and alkaline (NaOH) solutions to find the percentage of solids extraction with varying liquid-solid ratios, acid/alkali concentrations, reaction temperatures and retention time. For pyrolysis operation, UTS was selected among the four biomass samples with a set of pre-treatment parameters (4.81 wt. % H2SO4, 15:1 liquid-solid ratio, 4hr retention time, 70 ̊C, 100rpm agitation speed) that maximizes bio-oil production. Pyrolysis in a batch tubular furnace at 600 ̊C with nitrogen flowrate of 30 ml/min resulted in bio-oil yield of 39.43% and 27.67%, and char yield of 38.07% and 30.73% from raw and pre-treated UTS respectively. The semi-batch pyrolysis results were compared with biomass pyrolysis results from the batch pyrolysis rig operations. The catalytic upgrading of the bio-oil to liquid fuel in a batch reactor is ongoing research work. The contribution of this research can be summarised as the successful design, fabrication, testing and operation of a Fluidized Bed System to produce fuel from biomass in batch pyrolysis. Characterization of the feedstock to get the optimum operation condition of the designed FBR to get the best yield out of the system and evaluation of the performance characteristics (Mass and Energy Balance) of the system. Characterization of the products (bio-oil, bio-char and syngas) following standard methods having results comparable with literature

Development and assessment of a fast pyrolysis reactor for bio-oil, syngas and bio-char production from biomass residues

Design, development and assessment of a Fluidized Bed Reactor (FBR) is a very complex process, where enormous empirical correlations; charts and graphs; lot of parameters, assumptions, unit operations are involved, straight forward design equations and design data are limited, and generally the operation of the system requires many adjustments. The improved design of FBR with high coefficient of performance (COP), low energy consumption, high yield and environmentally friendly (low emission) is the target. The scope of the study is to design and fabrication of a lab scale fluidized bed fast pyrolysis system with throughput capacity of 1 kg of dry biomass per hour which includes a bubbling fluidized bed reactor, 2 cyclone separators in series, 4 condensers in series operating between temperatures of 600-300; 300-200; 200-125 and 125-40 ̊C to selectively condense alkanes, phenols, aromatics, indene, methyl-indene, benzene, toluene, methyl–naphthalene, esters, acids, alcohols, ketones; 2 heaters (1 pre-and 1 primary), an auger feeder with hopper and controller, blowers and rig structure. A 3-D simulation was performed to facilitate the mounting of different unit operations, instruments and control panels with sufficient maintenance and manoeuvring accessibilities yet compact structure with low structural footprints. The rig is having the dimensions of 2204X2750X1100mm (L x H x W) and suitable for batch operation to produce about 650 gm bio-oil, 150 gm non-condensable and 200 gm bio-char from 1kg of dry biomass pyrolysis. The rig is manually operated, however the data acquisition and logging systems are digital and has provision of scrubbing exhaust gas, and an online analyser has been installed to measure and monitor lower hydrocarbons including hydrogen concentrations and Lower Explosive Limit (LEL) in the exhaust gas. Four types of biomass such as Empty fruit bunch (EFB), Urban tree shavings (UTS), Saw dust Broga (SDB) and Saw dust Semenyih (SDS) were pre-treated with aqueous acidic (H2SO4) and alkaline (NaOH) solutions to find the percentage of solids extraction with varying liquid-solid ratios, acid/alkali concentrations, reaction temperatures and retention time. For pyrolysis operation, UTS was selected among the four biomass samples with a set of pre-treatment parameters (4.81 wt. % H2SO4, 15:1 liquid-solid ratio, 4hr retention time, 70 ̊C, 100rpm agitation speed) that maximizes bio-oil production. Pyrolysis in a batch tubular furnace at 600 ̊C with nitrogen flowrate of 30 ml/min resulted in bio-oil yield of 39.43% and 27.67%, and char yield of 38.07% and 30.73% from raw and pre-treated UTS respectively. The semi-batch pyrolysis results were compared with biomass pyrolysis results from the batch pyrolysis rig operations. The catalytic upgrading of the bio-oil to liquid fuel in a batch reactor is ongoing research work. The contribution of this research can be summarised as the successful design, fabrication, testing and operation of a Fluidized Bed System to produce fuel from biomass in batch pyrolysis. Characterization of the feedstock to get the optimum operation condition of the designed FBR to get the best yield out of the system and evaluation of the performance characteristics (Mass and Energy Balance) of the system. Characterization of the products (bio-oil, bio-char and syngas) following standard methods having results comparable with literature

Data-Centric Multiobjective QoS-Aware Routing Protocol for Body Sensor Networks

In this paper, we address Quality-of-Service (QoS)-aware routing issue for Body Sensor Networks (BSNs) in delay and reliability domains. We propose a data-centric multiobjective QoS-Aware routing protocol, called DMQoS, which facilitates the system to achieve customized QoS services for each traffic category differentiated according to the generated data types. It uses modular design architecture wherein different units operate in coordination to provide multiple QoS services. Their operation exploits geographic locations and QoS performance of the neighbor nodes and implements a localized hop-by-hop routing. Moreover, the protocol ensures (almost) a homogeneous energy dissipation rate for all routing nodes in the network through a multiobjective Lexicographic Optimization-based geographic forwarding. We have performed extensive simulations of the proposed protocol, and the results show that DMQoS has significant performance improvements over several state-of-the-art approaches

Adverse Selection in Community Based Health Insurance among Informal Workers in Bangladesh: An EQ-5D Assessment

Community-based Health Insurance (CBHI) schemes are recommended for providing financial risk protection to low-income informal workers in Bangladesh. We assessed the problem of adverse selection in a pilot CBHI scheme in this context. In total, 1292 (646 insured and 646 uninsured) respondents were surveyed using the Bengali version of the EuroQuol-5 dimensions (EQ-5D) questionnaire for assessing their health status. The EQ-5D scores were estimated using available regional tariffs. Multiple logistic regression was applied for predicting the association between health status and CBHI scheme enrolment. A higher number of insured reported problems in mobility (7.3%; p = 0.002); self-care (7.1%; p = 0.000) and pain and discomfort (7.7%; p = 0.005) than uninsured. The average EQ-5D score was significantly lower among the insured (0.704) compared to the uninsured (0.749). The regression analysis showed that those who had a problem in mobility (m 1.25–2.17); self-care (OR = 2.29; 95% CI: 1.62–3.25) and pain and discomfort (OR = 1.43; 95% CI: 1.13–1.81) were more likely to join the scheme. Individuals with higher EQ-5D scores (OR = 0.46; 95% CI: 0.31–0.69) were less likely to enroll in the scheme. Given that adverse selection was evident in the pilot CBHI scheme, there should be consideration of this problem when planning scale-up of these kind of schemes

Target coverage through distributed clustering in directional sensor networks

Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs). For guaranteed coverage and event reporting, the underlying mechanism must ensure that all targets are covered by the sensors and the resulting network is connected. Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead. Gathering nodes into clusters might provide a better solution to this problem. In this paper, we have designed distributed clustering and target coverage algorithms to address the problem in an energy-efficient way. To the best of our knowledge, this is the first work that exploits cluster heads to determine the active sensing nodes and their directions for solving target coverage problems in DSNs. Our extensive simulation study shows that our system outperforms a number of state-of-the-art approaches

Lifetime Maximization of Sensor Networks Through Optimal Data Collection Scheduling of Mobile Sink

The problem of maximizing lifetime of a sensor network is still challenging mainly due to the stringent delay-deadline of real-time applications and heterogeneity of sensor devices. The problem is further complicated when the network contains many obstacles. In maximizing network lifetime, existing literature works either merely address issues of application delay-deadline and presence of obstacles, or analyze primitive data collection approaches for such an environment. In this paper, we formulate optimal data collection schedule of a mobile sink in an obstructed sensor network as a mixed-integer linear programming (MILP) problem. The proposed data collection scheduling finds an optimal set of rendezvous nodes over a preformed Starfish routing backbone, and corresponding sojourn duration so as to maximize the network lifetime while maintaining delay-deadline constraint in an obstructed network. The proposed Starfish-scheduling ensures a loop-free traveling path for a mobile sink across the network. The results of performance evaluation, performed in network simulator-2, depict the suitability of Starfish scheduling as it outperforms state-of-the-art-works in terms of extending network lifetime and data delivery throughput as well as reducing average end-to-end delay

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts

Design and evaluation of a multihoming-based mobility management scheme to support inter technology handoff in PNEMO

Handof management is an indispensable component in supporting network mobility. The handof situation raises while the Mobile Router (MR) or Mobile Node (MN) crosses the diferent wireless communication access technologies. At the time of inter technology handof the multiple interface based MR can accomplish multihoming features such as enhanced availability, trafc load balancing with seamless fow distribution. These multihoming topographies greatly responsible reducing network delays during inter technology handof. This article proposes a multihoming based Mobility management in Proxy NEMO (MM-PNEMO) scheme that considers benefts of using multiple interfaces. To support the proposed scheme design a numerical framework is developed that will be used to assess the performance of the proposed MM-PNEMO scheme. The performance is evaluated in the state-of-art numerical simulation approach focusing the key success metrics of signalling cost and packet delivery cost, that eventually scaling the total handof cost. The numerical simulation result shows that the proposed MM-PENMO delightedly reduces the average handof cost to 60% compared to existing NEMO Basic support protocol (NEMO-BSP) and PNEMO