in Wiley Online Library (wileyonlinelibrary.com).Bacteria being disinfected in fluid media are discrete entities and mesoscopic in size; moreover, they are incessantly as well as irregularly in motion and in collision among themselves or with the surrounding solid surfaces. As such, it is highly likely that some of the attributes of the bacterial population, for example, their number concentration, will fluctuate randomly. This is especially the case at the tail-end of disinfection when the population of bacteria is sparse. It might be effectual, therefore, to explore the resultant random fluctuations via a stochastic paradigm. Proposed herein is a Markovian stochastic model for the rate of bacterial disinfection, whose intensity of transition takes into account the contact time of the bacteria with the disinfecting agent to eliminate any given percentage of the bacteria in terms of a nonlinear function of time. The model's master equation has been simulated by resorting to the Monte Carlo method to circumvent the undue complexities in solving it analytically or numerically via conventional numerical techniques. For illustration, the mean, the variance (standard deviation), and the coefficient of variation of the number concentration of bacteria during disinfection have been estimated through Monte Carlo simulation. The results of simulation compare favorably with the available experimental data as well as with those computed from the corresponding deterministic model.

This contribution presents a sequel to our previously published nonlinear stochastic model for bacterial disinfection whose intensity function is explicitly proportional to the contact time of the bacteria with the disinfecting agent. In the current model, the intensity function is proportional to the square of the contact time to account for an accelerated rate of a disinfection process. The model gives rise to the process’ master equation whose solution renders it possible to obtain the analytical expressions of the process’ mean, variance (or standard deviation), and coefficient of variation. Moreover, the master equation has been simulated via the Monte Carlo method, thereby yielding the numerical estimates of these quantities. The estimates’ values are compared with those computed via the analytical expressions; they are in excellent accord. They are also compared with the available experimental data as well as with the results obtained from our earlier model.

In this work, we propose a method and its concomitant software for the identification and assessment of building-evacuation routes. First, the building floor map is represented via P-graphs, thereby facilitating the identification of the evacuation routes. Second, each route identified is transformed into a time-expanded, process-network synthesis (PNST) problem, which can be algorithmically solved by the P-graph methodology. In the proposed method, each location and passage in the building is defined by a set of attributes to be taken into account in the evacuation-route planning. Third, the evacuation routes are ranked in terms of the evacuation time computed as the minimum cost of the corresponding PNST problem. Furthermore, the evacuation routes can be ranked according to specific criteria (e.g., bottlenecks, route utilization, etc.).Resumen-Este trabajo propone un método y software para la identificación y análisis de los planes de rutas de evacuación en edificios. Inicialmente, el plano arquitectónico del edificio se representa mediante P-graphs para facilitar la identificación de las rutas de evacuación. Posteriormente, cada una de estas rutas se transforma en un problema de síntesis de redes de procesos de tiempo expandido (PNST); el cual se resuelve algorítmicamente con base en la metodología P-graph. En el método propuesto, cada ubicación y corredor en el edificio se describe por medio de un conjunto de atributos que debe considerarse en el plan de rutas de evacuación. Finalmente, las rutas de evacuación se organizan con base en el tiempo de evacuación que se calcula como el costo mínimo del correspondiente problema PNST. Además, las rutas de evacuación pueden organizarse según diferentes criterios (e.g., cuellos de botella, utilización de rutas, etc.).

Thermal treatment under controlled conditions results in almost complete destruction, or disinfection, of cell or bacterial populations. Medical needs and public health concerns often demand that such disinfection be as complete as possible. The bacteria, particulate and mesoscopic in size, undergo complex motion and behavior, and thus, they die off at irregular rates during disinfection. Hence, in addition to the average death rates of bacteria, the fluctuations around these average rates must be known, especially in the final period, or tail end, of disinfection where the number concentration of bacteria becomes exceedingly low, thereby magnifying the fluctuations.It is natural and often desirable that various aspects of a process involving mesoscopic, particulate entities undergoing complex motion and behavior, such as the aforementioned bacterial disinfection, be explored on the basis of the statistical framework or a stochastic paradigm. Such aspects can range from the analysis and modeling of the particulate entities involved in the process to the optimization and control of the process itself. 1-6 Unambiguous discourses have been given as to the need of statistical or stochastic treatment of bacteriapopulation dynamics in disinfection. 7,8 This note is concerned with the stochastic analysis and modeling of thermal disinfection of bacteria as a pure-death process based on a nonlinear mechanistic rate law, that is, the logistic law. Hitherto, the linear (that is, first-order) rate law has been predominantly adopted for stochastically analyzing and modeling the thermal disinfection rate of bacteria. 9-13 Nevertheless, the growth-positive or negative-of biological populations, including microbial populations, has been increasingly formulated according to nonlinear mechanistic rate laws, 14-18 such as the logistic law. 19,20 In this regard, Soboleva and Pleasants 21 incorporated the logistic law into the modeling of the growth of a biological population. In their work, the mean and variance of the process were computed numerically from the Fokker-Plank equation under the assumption that the random variable characterizing the process-the size or number density of a discrete biological population-is continuous. The adoption of the logistic law has been proposed for modeling the death rate of microorganisms in thermal disinfection 22 ; apparently, no attempt has been made to incorporate this law into the stochastic formulation because of the computational complexity arising from its nonlinearity. Herein, this complexity is circumvented by resorting to the system-size expansion, a rational or logical approximation method, of the master equation of the process. 2,23,24 The numerical results are compared with the available experimental data obtained with S. aureus 25 as well as with those obtained with M. paratuberculosis. 26 Model FormulationThe system under consideration is the population of bacteria that are being thermally deactivated. The population decreases as a result of the death of bacteria, one at a time, ...

At the outset, the design of an organization-based multiagent system is modeled according to the framework of Organization Model for Adaptive Complex Systems (OMACS). Subsequently, this design model is transformed into a process-network model.Eventually, the resultant process-network model in conjunction with the P-graph-based methodology give rise to: (i) the maximal structure of the process network, comprising all the feasible combinations of structures, i.e., OMACS-based design configurations, capable of yielding the specified products from the specified raw material; (ii) every feasible structure for the process of interest; and (iii) the optimal structure of the network, i.e., the optimal OMACS-based design configuration. Finally, in light of the tenet of a modelingtransformation-evaluation paradigm, an appraisal is made of the feasibility as well as the flexibility and cost of the optimal OMACS-based design configuration obtained.

The production of renewable fuels and chemicals is a critical component of global strategies to reduce greenhouse gas emissions. In this regard, pyrolysis oil obtained from biomass comprises hundreds of chemical compounds, thus rendering it a good precursor for manufacturing a variety of fuel products of commercial interest. Despite the large number of contributions describing the products’ extraction, upgrading, and potential refining schemes, no bio-oil refinery is currently in operation. The main challenge in building a bio-oil refinery lies in the lack of an economically viable process configuration. Systematic studies comparing alternative refinery concepts, or configurations, are needed to identify the most promising configuration. To the best of our knowledge, this study is the first to use process graph (P-graph) methodology for the synthesis of pyrolysis oil refineries. In particular, this work shows the effectiveness of P-graph methodology in simultaneously calculating the profitability of various biorefinery designs by using data reported in the literature and providing information on how the introduction of new technologies to the database will impact the formation of profitable biorefinery concepts. Our work demonstrates a methodology for the addition of new unit operations to the database generated from the literature. The addition of a centrifuge for water extraction and a wet oxidation system for acetic acid production resulted in the generation of 330 biorefinery configurations, seven of which have a profitability ranging from $1,650 to $23,666/h (USD) with acetic acid and levoglucosan as the main products, respectively. This demonstrates that P-graph methodology is useful for discovering optimum techno-economic scenarios that may otherwise be overlooked.

Activated carbons (ACs) have been widely deployed in the purification of gases and liquids or the separation of their mixtures. The formation of ACs entails the modification of the original internal surfaces of carbonaceous substrates, for example, coal or biomass, which can be effected by a variety of chemical or physical methods, thereby augmenting the carbonaceous substrates' adsorbing capacities. The formation of ACs tends to proceed randomly or stochastically in view of the discrete and mesoscopic nature of the carbonaceous substrates, as well as the random encounters between the activation agent and carbon on the carbonaceous substrates' internal surfaces; in addition, the carbonaceous substrates' internal surfaces exhibit an intricate morphology or structure. Naturally, these traits of the formation of ACs render the process to vary incessantly with time. Thus, it is highly desirable that the analysis, modeling, and simulation of the formation of ACs from carbonaceous substrates be performed in light of a stochastic paradigm. Herein, a stochastic model for the formation of ACs is formulated as a pure-death process based on a nonlinear intensity of transition. The model gives rise to the process' nonlinear master equation whose solution is obtained by resorting to a rational approximation method, the system-size expansion. This solution renders it possible to compute the mean as well as higher moments about this mean, for example, variance or standard deviation, which reveal and quantify the process' inherent fluctuations. The results of modeling are validated by comparing them with the available experimental data.

Molecular sieves, including carbon molecular sieves (CMSs) manufactured from activated carbons, can be variously applied in the purification and separation of gaseous and liquid mixtures and can also serve as catalysts. CMSs are formed by depositing fine particles of carbon on the mouths of pores of such activated carbons. These carbon particles are generated by decomposing a gaseous carbon source. The formation of CMSs proceeds stochastically, which is mainly attributable to the mesoscopic sizes and complex motion of the depositing carbon particles and random distribution of the pores on the activated carbons. In this work, CMS formation is modeled as a pure-birth process with a linear intensity of transition. The resultant model gives rise to the governing equations for the mean and variance. The model is validated by comparing the analytical solutions of these equations with available experimental data. The mean value of the model is in excellent accord with the data. In addition, the kinetic constants resulting from the model have been found to obey the Arrhenius law.