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Processes, Volume 6, Issue 3 (March 2018)

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Cover Story (view full-size image) Hydrogen production from biomass is sustainable and green. The conventional process for producing [...] Read more.
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Open AccessArticle Using Field Data for Energy Efficiency Based on Maintenance and Operational Optimisation. A Step towards PHM in Process Plants
Processes 2018, 6(3), 25; https://doi.org/10.3390/pr6030025
Received: 17 January 2018 / Revised: 7 March 2018 / Accepted: 7 March 2018 / Published: 11 March 2018
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Abstract
Energy saving is an important issue for any industrial sector; in particular, for the process industry, it can help to minimize both energy costs and environmental impact. Maintenance optimization and operational procedures can offer margins to increase energy efficiency in process plants, even
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Energy saving is an important issue for any industrial sector; in particular, for the process industry, it can help to minimize both energy costs and environmental impact. Maintenance optimization and operational procedures can offer margins to increase energy efficiency in process plants, even if they are seldom explicitly taken into account in the predictive models guiding the energy saving policies. To ensure that the plant achieves the desired performance, maintenance operations and maintenance results should be monitored, and the connection between the inputs and the outcomes of the maintenance process, in terms of total contribution to manufacturing performance, should be explicit. In this study, a model for the energy efficiency analysis was developed, based on cost and benefits balance. It is aimed at supporting the decision making in terms of technical and operational solutions for energy efficiency, through the optimization of maintenance interventions and operational procedures. A case study is here described: the effects on energy efficiency of technical and operational optimization measures for bituminous materials production process equipment. The idea of the Conservation Supply Curve (CSC) was used to capture both the cost effectiveness of the measures and the energy efficiency effectiveness. The optimization was thus based on the energy consumption data registered on-site: data collection and modelling of the relevant data were used as a base to implement a prognostic and health management (PHM) policy in the company. Based on the results from the analysis, efficiency measures for the industrial case study were proposed, also in relation to maintenance optimization and operating procedures. In the end, the impacts of the implementation of energy saving measures on the performance of the system, in terms of technical and economic feasibility, were demonstrated. The results showed that maintenance optimization could help in reaching an energy costs recovery equal to the 10% of the total costs for an electric motor system. Full article
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Open AccessEditorial Special Issue: Combined Scheduling and Control
Processes 2018, 6(3), 24; https://doi.org/10.3390/pr6030024
Received: 1 March 2018 / Revised: 2 March 2018 / Accepted: 2 March 2018 / Published: 7 March 2018
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Abstract
This Special Issue (SI) of Processes, “Combined Scheduling and Control,” includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison
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This Special Issue (SI) of Processes, “Combined Scheduling and Control,” includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates the creation of new applications and long-term sustainment of benefits.[...] Full article
(This article belongs to the Special Issue Combined Scheduling and Control) Printed Edition available
Open AccessFeature PaperArticle Fuel Gas Network Synthesis Using Block Superstructure
Processes 2018, 6(3), 23; https://doi.org/10.3390/pr6030023
Received: 2 February 2018 / Revised: 24 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
Cited by 3 | Viewed by 2022 | PDF Full-text (1394 KB) | HTML Full-text | XML Full-text
Abstract
Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address FGN synthesis problems using a block superstructure representation that was originally proposed for process design and intensification. The blocks interact
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Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address FGN synthesis problems using a block superstructure representation that was originally proposed for process design and intensification. The blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all nonadjacent blocks. The blocks with external feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. An additional layer of blocks are added as pools when there exists intermediate operations among source and sink blocks. These blocks can be arranged in a I × J two-dimensional grid with I = 1 for problems without pools, or I = 2 for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate FGN synthesis problem as a mixed-integer nonlinear (MINLP) formulation to optimally design a fuel gas network with minimal total annual cost. We revisit a literature case study on LNG plants to demonstrate the capability of the proposed approach. Full article
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Open AccessArticle Bleaching of Neutral Cotton Seed Oil Using Organic Activated Carbon in a Batch System: Kinetics and Adsorption Isotherms
Processes 2018, 6(3), 22; https://doi.org/10.3390/pr6030022
Received: 9 December 2017 / Revised: 17 February 2018 / Accepted: 22 February 2018 / Published: 1 March 2018
Cited by 1 | Viewed by 2014 | PDF Full-text (1817 KB) | HTML Full-text | XML Full-text
Abstract
In the processing of cotton and neem seeds to obtain oil for diverse uses, enormous quantities of seed husk are generated as waste, which when not properly disposed of, poses environmental problems. One way of reducing this waste is to use it for
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In the processing of cotton and neem seeds to obtain oil for diverse uses, enormous quantities of seed husk are generated as waste, which when not properly disposed of, poses environmental problems. One way of reducing this waste is to use it for the production of activated carbon (AC) for its multiple applications. In this work, activated carbon was produced from cotton and neem seed husks by carbonization followed by acid activation. The prepared ACs were characterized for its porosity and surface properties as well as for its ability to bleach neutral cotton seed oil. The prepared ACs are very efficient in the decoloration process, as they removed about 96–98% of the pigments compared to 98.4% removal with commercial bleaching earth. Temperature had a pronounced effect on the bleaching of neutral cotton seed oil. Maximum adsorption was observed at 60 °C for a contact time of 45 min. The adsorption kinetics were modelled by the intra-particle and the pseudo-second order equations while the adsorption isotherms followed the Langmuir and Freundlich equations. It is concluded that the organic ACs are efficient in pigment removal from neutral cotton seed oil and therefore are potential bleaching agents for the vegetable oil industry. Full article
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Open AccessFeature PaperArticle Mathematical Modeling and Parameter Estimation of Intracellular Signaling Pathway: Application to LPS-induced NFκB Activation and TNFα Production in Macrophages
Processes 2018, 6(3), 21; https://doi.org/10.3390/pr6030021
Received: 28 January 2018 / Revised: 10 February 2018 / Accepted: 21 February 2018 / Published: 25 February 2018
Cited by 4 | Viewed by 2193 | PDF Full-text (1240 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Due to the intrinsic stochasticity, the signaling dynamics in a clonal population of cells exhibit cell-to-cell variability at the single-cell level, which is distinct from the population-average dynamics. Frequently, flow cytometry is widely used to acquire the single-cell level measurements by blocking cytokine
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Due to the intrinsic stochasticity, the signaling dynamics in a clonal population of cells exhibit cell-to-cell variability at the single-cell level, which is distinct from the population-average dynamics. Frequently, flow cytometry is widely used to acquire the single-cell level measurements by blocking cytokine secretion with reagents such as Golgiplug. However, Golgiplug can alter the signaling dynamics, causing measurements to be misleading. Hence, we developed a mathematical model to infer the average single-cell dynamics based on the flow cytometry measurements in the presence of Golgiplug with lipopolysaccharide (LPS)-induced NF κ B signaling as an example. First, a mathematical model was developed based on the prior knowledge. Then, average single-cell dynamics of two key molecules (TNF α and I κ B α ) in the NF κ B signaling pathway were measured through flow cytometry in the presence of Golgiplug to validate the model and maximize its prediction accuracy. Specifically, a parameter selection and estimation scheme selected key model parameters and estimated their values. Unsatisfactory results from the parameter estimation guided subsequent experiments and appropriate model improvements, and the refined model was calibrated again through the parameter estimation. The inferred model was able to make predictions that were consistent with the experimental measurements, which will be used to construct a semi-stochastic model in the future. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessArticle Methanol Synthesis: Optimal Solution for a Better Efficiency of the Process
Processes 2018, 6(3), 20; https://doi.org/10.3390/pr6030020
Received: 31 January 2018 / Revised: 15 February 2018 / Accepted: 16 February 2018 / Published: 25 February 2018
Cited by 1 | Viewed by 2479 | PDF Full-text (5464 KB) | HTML Full-text | XML Full-text
Abstract
In this research, an ANOVA analysis and a response surface methodology are applied to analyze the equilibrium of methanol reaction from pure carbon dioxide and hydrogen. In the ANOVA analysis, carbon monoxide composition in the feed, reaction temperature, recycle and water removal through
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In this research, an ANOVA analysis and a response surface methodology are applied to analyze the equilibrium of methanol reaction from pure carbon dioxide and hydrogen. In the ANOVA analysis, carbon monoxide composition in the feed, reaction temperature, recycle and water removal through a zeolite membrane are the analyzed factors. Carbon conversion, methanol yield, methanol productivity and methanol selectivity are the analyzed responses. Results show that main factors have the same effect on responses and a common significant interaction is not present. Carbon monoxide composition and water removal have a positive effect, while temperature and recycle have a negative effect on the system. From central composite design, an optimal solution is found in order to overcome thermodynamic limit: the reactor works with a membrane at lower temperature with carbon monoxide composition in the feed equal to 10 mol % and without recycle. In these conditions, carbon conversion, methanol yield, methanol selectivity, and methanol production are, respectively, higher than 60%, higher than 60%, between 90% and 95% and higher than 0.15 mol/h when considering a feed flow rate of 1 mol/h. A comparison with a traditional reactor is also developed: the membrane reactor ensures to have a carbon conversion higher of the 29% and a methanol yield higher of the 34%. Future researches should evaluate an economic analysis about the optimal solution. Full article
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Open AccessFeature PaperArticle Green Hydrogen Production from Raw Biogas: A Techno-Economic Investigation of Conventional Processes Using Pressure Swing Adsorption Unit
Processes 2018, 6(3), 19; https://doi.org/10.3390/pr6030019
Received: 23 December 2017 / Revised: 14 February 2018 / Accepted: 15 February 2018 / Published: 25 February 2018
Cited by 1 | Viewed by 2587 | PDF Full-text (2373 KB) | HTML Full-text | XML Full-text
Abstract
This paper discusses the techno-economic assessment of hydrogen production from biogas with conventional systems. The work is part of the European project BIONICO, whose purpose is to develop and test a membrane reactor (MR) for hydrogen production from biogas. Within the BIONICO project,
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This paper discusses the techno-economic assessment of hydrogen production from biogas with conventional systems. The work is part of the European project BIONICO, whose purpose is to develop and test a membrane reactor (MR) for hydrogen production from biogas. Within the BIONICO project, steam reforming (SR) and autothermal reforming (ATR), have been identified as well-known technologies for hydrogen production from biogas. Two biogases were examined: one produced by landfill and the other one by anaerobic digester. The purification unit required in the conventional plants has been studied and modeled in detail, using Aspen Adsorption. A pressure swing adsorption system (PSA) with two and four beds and a vacuum PSA (VPSA) made of four beds are compared. VPSA operates at sub-atmospheric pressure, thus increasing the recovery: results of the simulations show that the performances strongly depend on the design choices and on the gas feeding the purification unit. The best purity and recovery values were obtained with the VPSA system, which achieves a recovery between 50% and 60% at a vacuum pressure of 0.1 bar and a hydrogen purity of 99.999%. The SR and ATR plants were designed in Aspen Plus, integrating the studied VPSA model, and analyzing the behavior of the systems at the variation of the pressure and the type of input biogas. The SR system achieves a maximum efficiency, calculated on the LHV, of 52% at 12 bar, while the ATR of 28% at 18 bar. The economic analysis determined a hydrogen production cost of around 5 €/kg of hydrogen for the SR case. Full article
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