Industrial Process Units Research Articles

OVERVIEW OF ACEH TUNA EXPORT COMMODITY: POLICY PAPER

This study is part of a policy paper that aims to provide an overview of tuna exports in Aceh. It will examine the competitiveness of fresh, chilled, or frozen tuna commodities and the impact of their export value on the GDP of the fisheries sub-sector in Aceh. The findings will propose strategies for the development and management of tuna exports in Aceh. The study found that the competitiveness of Aceh's fresh, chilled, or frozen tuna commodities is low, with an RCA value of less than 1. Additionally, an increase in tuna export value did not lead to an increase in the GDP of Aceh's fisheries sub-sector. Strategies suggested for developing tuna exports in Aceh include improving logistics processes and cold chain systems at ports, enhancing the potential of processed tuna products like canned tuna and tuna loin, developing medium to large-scale Industrial Processing Units for canned and loin tuna, raising quality standards through regular audits and inspections of local producers implementing HACCP, optimizing the use of fiscal incentives for exporters to help cover the cost of rising export cargo rates, strengthening partnerships with start-ups for trading missions of tuna in the global market at more competitive prices, and enhancing and developing the branding of fresh, frozen, chilled, and processed tuna products through joint councils and chambers of commerce of tuna importing countries.

Neural Network Weight Comparison for Industrial Causality Discovering and Its Soft Sensing Application

Due to the complex reaction mechanisms of industrial process units, causality and correlations exist between industrial process variables. Causal discovery algorithms have been utilized to discover the knowledge on variable relationships and guide process modeling and control optimization. However, most of them are limited by strict assumptions, such as linear relationships, additive noise, steady-state process, etc. Therefore, these methods cannot gain good performance for most practical industrial processes. To solve these problems, a novel weight comparison causal mining (WCCM) algorithm is proposed in this paper for industrial causal graph discovery. It first trains a group of hidden layer neural networks with process data, then mines an undirected skeleton of the process variables according to the comparison of the network weights, and further determines the causal directions of the undirected edges in the skeleton to get a directed causal graph. The effectiveness of WCCM is verified on a benchmark and a practical industrial case from the Urea Synthesis process. The undirected and direct edges mined by WCCM show high consistency with the ground truths. Moreover, the causal discovery results of WCCM is utilized to guide the feature selection of soft sensor modeling, resulting in improved prediction accuracy and enhanced model interpretability.

Energy optimization and neural-based dynamic analysis of integrated multiple stage evaporator

Abstract Minimizing energy consumption is often a grave challenge in many industrial energy-intensive process units such as Multiple Stage Evaporator (MSE). Integration of Energy Reduction Schemes (ERSs) is a common technique to resume a countable amount of energy. Hence, the present work proposes a hybrid (h-) ERSs integrated MSE placed in the paper industry, used to increase the solid content of the black liquor. The h-ERSs Integrated MSE comprises several ERSs such as Thermo-Vapor Compressor, Steam-, Feed-split, and Feed Preheater to improve the energy efficiency significantly, and its energy performance is compared with base (b-) MSE. For this purpose, nonlinear mathematical models have been developed and transformed into a constrained optimization problem to search for the optimum energy efficiency obtained as Steam Economy (SE). A state-of-art metaheuristic approach, Equilibrium Optimizer (EO), along with some well-acquainted solution approaches (Interior Point OPTimizer, Interior Point Method, and Particle Swarm Optimization) has been simulated in different platforms to estimate the maximum SE to check their competitiveness for this industrial optimization problem. It is observed that EO outperformed all the algorithms with a 66 % higher SE for h-MSE than b-MSE. Eventually, the steady state parameters are applied as the initial conditions to analyze the nonlinear enthalpy dynamics of the b-and h-MSE. A neural base solution has been adopted to rigorously study the open-loop process dynamics that meet the desired product quality.

Three pathways towards elimination of CO2 emissions from industrial plants that use hydrocarbon fuels

Many industrial plants burn hydrocarbon fuels to generate electricity and high-temperature thermal energy required in their processes. This work proposes a novel system for generation of such thermal energy while eliminating CO2 emissions. The system produces electricity, thermal energy, methanol, and carbon black. Combined heat and power (CHP) unit generates high-temperature thermal energy and electricity required by the industrial plant; CO2 produced by CHP and by the plant itself is converted to methanol by using hydrogen produced via pyrolysis of methane, which has recently advanced to the first industrial scale plant. Thermal energy contained in the CHP flue gas replaces utility plant boilers or furnaces of the industrial plant (e.g. a refinery or a petrochemical plant). Two pathways are based on two different methane pyrolysis methods and CO2 removal via MEA absorption. The third pathway replaces MEA absorption with a novel in situ CO2 conversion reactor. Proposed pathways are examined as ways of eliminating CO2 emissions from a 5 million ton of crude oil per annum refinery. The study includes operational costs, capital costs, and material prices in USA, EU, and China. The results show that the proposed pathways are profitable for USA and EU, while for China they are not. The proposed pathways provide CO2 free energy generation from natural gas as an alternative to energy generation from either solar, wind or hydro energy sources. In addition, they enable capture and conversion of CO2 emissions from industrial process units.

A predictive toolset for the identification of degradation pattern and toxic hazard estimation of multimeric hazardous compounds persists in water bodies

An array of industrial processing units generates many multimeric hazardous compounds, such as complex technical lignin and its toxic derivatives, thereby persist in expelled water bodies. The inclusion of some group of motifs in the complex technical lignin structure helps it resist degrade biologically, most often even recalcitrant. Relatively small concentrations of lignin are harmful to aquatic organisms and can trigger environmental hazards. Sadly, the entire biotransformation pathway and insightful information about these toxic derivatives are incomplete and missing in the literature. This is mainly because the current conventional treatments often fail to identify all transformed compounds and their environmental fate. Thus, a robust toolset is much needed to cover this literature gap. Inadequate performance of conventional remediation processes and biological degradation patterns can be maximally optimized with the aid of predictive toolset methods that could offer better degradability and complete transformed compound information. A predictive toolset-assisted biodegradation pattern determination is a multifaceted and reliable analytical technique that can help to overcome existing shortcomings by providing an entire transformation pathway. Considering the above critiques, this work reports on the degradation pattern, and toxicological endpoints of five hazardous compounds, i.e., 2-chlorosyringaldehyde, 5-chlorovanillin, catechol, guaiacyl 4-O-5 guaiacyl, and syringyl β-O-4 syringyl β-O-4 sinapyl alcohol, that persists in water matrices. The predictive transformation pattern was revealed notably less complex end-products of catechol as; succinate, and 2-Oxo-4-pentenoate. The gastrointestinal (GI) absorption rate was found high for all tested compounds, excluding trimer compound, i.e., syringyl β-O-4 syringyl β-O-4 sinapyl alcohol. The toxicity and persistence profile tested via Toxtree showed that the Cramer Rules, Verhaar Scheme, and Structural Alerts for Reactivity, (START) biodegradation ability as positive, and all five target compounds were found as class-II persistent compounds. Furthermore, the Ecological Structure-Activity Relationships (ECOSAR)assisted testing specifies that all tested derivatives have multiple aquatic toxic levels. In summary, the current findings endorse the hazardous compounds and undertake prescreening of the deprivation policy to protect the environment.

English

This study assessed handling practices and microbiological contamination of salted sun-dried Nile Perch, Lates niloticus, commonly known as Kayabo in Tanzania. The processors of Kayabo were small-scale processors located at Kanyama and Mwaloni, Mwanza. Mixed methods (observations, face-to-face interviews, and microbiological sampling) were used to assess handling practices of processors and microbiological quality of Kayabo. Hierarchical cluster analysis on handling practices of Kayabo processors produced three distinct clusters of 15 (Cluster I), 10 (Cluster II), and 5 (Cluster III) processors. In general, the majority of processors had inadequate training on appropriate food handling, lack of hygienically designed facilities (building and cooling), poor sanitation, and the use of low-quality raw materials (rejects from industrial processing units). Assessment of microbiological quality of Kayabo indicated a significant (P<0.05) variation in Total Viable Counts, TVC (2.08 - 8.68 Log CFU/25 g), and Staphylococcus aureus (<1-5.49 log CFU/25 g) among the clusters. About 80% (12/15) and 42% (5/12) of the processors in 'Cluster I' exceeded the legal limits for TVC (5 Log CFU/25 g) and S. aureus (3 Log CFU/25 g), respectively. Strikingly, Escherichia coli and Listeria monocytogenes were below the quantification limits. Intervention measures for improvement on the raw materials, structure, and infrastructure, training, and adoption of good practices across the value chain are urgently needed to ensure the quality and safety of Kayabo.    Key words: Fish, salted sundried nile perch, handling practices, microbiological quality, Kayabo, Lake Victoria.

A virtual laboratory for radiotracer and sealed-source applications in industry

Abstract Radioactive sealed sources and radiotracer techniques are used to diagnose industrial process units. This work introduces a workspace to simulate four sealed sources and radiotracer applications, namely, gamma scanning of distillation columns, gamma scanning of pipes, gamma transmission tomography, and radiotracer flow rate measurements. The workspace was created in Geant4 Application for Tomographic Emission (GATE) simulation toolkit and was called Industrial Radioisotope Applications Virtual Laboratory. The flexibility of GATE and the fact that it is an open-source software render it advantageous to radioisotope technology practitioners, educators, and students. The comparison of the simulation results with experimental results that are available in the literature showed the effectiveness of the virtual laboratory.

Simulation of heat transfer in fire-tube and fire-tube-contact water heaters

A mathematical model is proposed for calculating the constructive and operational characteristics of fire-tube and fire-tube-contact storage heaters used in the industrial, potable and firefighting water processing and storage units. The mathematical model includes equations for calculating heat balance, heat transfer, radiant heat transfer, and physical and chemical properties of substances. The heat transfer in fire tubes is calculated using a generalized process characteristic that is a function of structural and parametric indicators and heat engineering criteria. An example of application of the simulation software package for designing water heaters and conducting numerical experiments in order to draw up process flow diagrams accounting for variation of operational parameters and composition of fuel gas, is presented.

Experimental study and mathematical model of coagulation/sedimentation units for treatment of food processing wastewater

A model for coagulation/sedimentation was developed; based on experimental observations to investigate the removal of COD and TSS from the food processing wastewater. The experimental study was conducted using three coagulants: ferric chloride, ferrous sulfate, and aluminum sulfate. The wastewater samples were collected from an industrial food processing unit. Ferrous sulfate and aluminum sulfate are more efficient coagulants compared to ferric chloride. The removal efficiency of COD and TSS using alum were 79% and 92%, respectively. The removal efficiency of COD and TSS using FeCl3 were 73% and 93%, respectively. The removal efficiency of COD and TSS using ferrous sulfate were 79% and 93%, respectively.An empirical model was developed to evaluate the removal efficiency of the system. Different equations were developed for each coagulant. The model parameters were optimized using experimental observations. Statistical analysis indicated that the developed model can describe the experimental data accurately. The coefficient of determination (R2) values shows that the model can represent the experimental data with reasonable accuracy.

An integrated hybrid renewable energy system: potential of electricity—a geospatial approach

The sustainable economic advancement of a nation stands on an adequate supply of electrical energy. Reduction in cost for industrial processing units has been pulled with the wide utilization of rice husk (RH) and solar energy as value-added renewable energy sources for the era of power generation. The principal reason is to assist the study area towards development and to build up an integrated hybrid renewable power plant in rural regions in India. In this study, a new solar town concept and integrated biomass can be served as a hybrid global model for a smart eco-village in tropical countries like India. The demand, properties and cost of RH generation from rice mills resemble substitution of coal in power generation. Solar energy and agricultural residues are seasonally accessible sources and loosely dispersed over the substantial topographical area. So, for such an assessment, satellite image is a handy input and the preciseness of estimation increases with a high-resolution image. The methodology has been implemented for optimal sizing of an integrated renewable energy system-based power generation plants in India with less investment and minimum power cost. The outcome of the study reveals that the efficient use of renewable energy sources and proposal for placing a power generation plant require cautious identification of various techno-economic parameters. By considering the power crisis in India, the findings of these works are expected to assist the policymakers in the decision-making process. Particularly, this paper generates data on village-level RH availability from rice mills and subsequently potential electric power capacity for a particular region.

Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics

There has been a growing trend to couple different levels of modelling, such as going from first-principle calculations to the meso (e.g. kinetic Monte Carlo - KMC) and macro scale (e.g. computational fluid dynamics - CFD). In the current investigation, we put forward a CFD study of CO2 hydrogenation to methanol for heterogeneous reacting flows in reactors with complex shape geometries, coupled with first-principle calculations (density functional theory - DFT). KMC operation simulations were also performed to obtain insight into the uppermost layer conditions during the reaction. With computational fluid dynamics, the focus was placed on the non-uniform catalytic reduction of carbon dioxide to formate, which we treated with a detailed mean-field first-principle microkinetic model, analysed, and corroborated with experiments. The results showed a good consistent agreement with experimental data. The formulated methodological approach paves the way towards full virtual multiscale system descriptions of industrial processing units, encompassing all conventional stages, from catalyst design to the optimization of mass transfer parameters. Such a bridging is outlined for carbon capture and utilization.

A New simplified calculation model of geometric thermal features of a vertical propane jet fire based on experimental and computational studies

Jet fires and their consequences are one of the significant factors responsible for catastrophic events in industrial process units. In this research, various types of turbulence models were investigated to simulate a vertical propane jet fire using computational fluid dynamics (CFD). CFD was applied to evaluate the following turbulence models of k-ε, SST, BSL, BSL RS and the Realizable k-epsilon (RNG k-ε), the Eddy Dissipation Concept (EDC) for combustion, and the Monte Carlo model for radiation. All the above-mentioned turbulence models are used for a temperature range of 1500–1700K. The results showed that the SST turbulence model is the best option, with average error of 4.7 % for a jet fire simulation, with a lower computational time. The simulation of the jet fire shape at surface temperature of 800K was also compared with the experimental data obtained under the same conditions. By taking into account the time parameter in the simulation, the predicted ratio of flame length and equivalent flame diameter results are in good agreement with the experimental jet fire data.

Digital Twin for Monitoring of Industrial Multi-Effect Evaporation

Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this context, the present manuscript describes the successful implementation of a digital twin to represent a four-stage multi-effect evaporation train from an industrial sugar-cane processing unit. Particularly, the complex phenomenological effects, including the coupling between thermodynamic and fluid dynamic effects, and the low level of instrumentation in the plant constitute major challenges for adequate process operation. For this reason, dynamic mass and energy balances were developed, implemented and validated with actual industrial data, in order to provide process information for decision-making in real time. For example, the digital twin was able to indicate failure of process sensors and to provide estimates for the affected variables in real time, improving the robustness of the operation and constituting an important tool for process monitoring.

Early malfunction diagnosis of industrial process units utilizing online linear trend profiles and real‐time classification

SummaryThe early detection of potential malfunctions at process systems can significantly reduce downtime and improve their overall operability. In that context, this paper demonstrates the behavior and response, through a comparative analysis, of novel data‐driven diagnosis methods for interdependent time series. The proposed real‐time slope statistic profile method utilizes a self‐adaptive sliding window based on a real‐time classification technique of linear trend profiles of both interdependent time series and internal condition so as to avoid misdetections. The calculation of the linear trend profile is based on a standard parametric linear trend test, and the selection of possible incidents is based on its two‐level cross‐checking. All possible combinations for the calculation of the trend test and cross‐checking are created to explore their efficiency. The proposed methods are tested against real data sets from a chemical process system of the Centre for Research and Technology Hellas/Chemical Process Energy and Resources Institute derived from specific scenarios during nominal operating conditions.

Dielectric Properties and Oxidation Roasting of Molybdenite Concentrate by Using Microwave Energy at 2.45 GHz Frequency

Conversion of electromagnetic energy into heat depends largely on the dielectric properties of the material being treated. Therefore, determining the dielectric properties of molybdenite concentrate and its microwave power penetration depth in relation to a temperature increment at the commercial frequency of 2.45 GHz is necessary to design industrial microwave processing units. In this study, the dielectric constants increased as the temperature increased in the entire experimental range. The loss factor presented an opposite trend, except for 298 K to 373 K (25 °C to 100 °C) in which a cavity perturbation resonator was used. The plots of nonlinear surface fitting indicate that the increase in dielectric loss causes a considerable decrease in penetration depth, but the dielectric constants exert a small positive effect. The thermogravimetric analysis (TGA-DSC) of the molybdenite concentrate was carried out to track its thermal decomposition process, aim to a dielectric analysis during the microwave heating. MoO3 was prepared from molybdenite concentrate through oxidation roasting in a microwave heating system and a resistance furnace, respectively. The phase transitions and morphology evolutions during oxidation roasting were characterized through X-ray diffraction and scanning electron microscopy. Results show that microwave thermal technique can produce high-purity molybdenum trioxide.

Theoretical and Experimental Investigation of Film Drainage Time

Abstract In many industrial process units for dispersed liquid-liquid and gas-liquid flows, understanding of the fluid particle breakup and coalescence phenomena are important for optimal process operation. These phenomena do not only dominate the fluid particle size distributions, but also directly affect the mass, momentum and heat transfer through the renewal surface and thus the contact area. This work focuses on the coalescence process between two fluid particles; in particular the coalescence (film drainage) time which appears in some closures used to close the coalescence terms of the population balance equation (PBE). The coalescence time of different drop-drop and liquid-liquid systems were investigated experimentally by micromanipulator technique. Further, the derivation of the coalescence time model used in the popular coalescence closure by Prince and Blanch [1] has been analyzed. The results show significant deviation between the model prediction and experimental data of coalescence time.

Evaluation of Weighted Residual Methods for the Solution of a Population Balance Model Describing Bubbly Flows: The Least-Squares, Galerkin, Tau, and Orthogonal Collocation Methods

In dispersed gas–liquid flows, the bubble size distribution plays an important role in the phase structure and interphase forces, which, in turn, determine the multiphase hydrodynamic behaviors, including the spatial profiles of the gas fraction, gas and liquid velocities, and mixing and mass-transfer behaviors. Thus, fluid particle coalescence and breakage phenomena are important for optimal operation of many industrial process units like the bubble column reactors. The population balance equation (PBE) is considered a concept for describing the evolution of populations of countable entities such as the bubbles in the bubble column. In recent studies, the least-squares method has been adopted for the solution of population balance (PB) problems. A favorable property of the weighted residual methods such as the least-squares technique is that the solution of the density function itself can be obtained from the fundamental PBE formulation, that is, not moment formulations. Hence, in this framework, the inn...

Assaí – An energy view on an Amazon residue

This paper analyzed the technical and economic feasibility of electricity generation using the residues from the exploitation of Assaí, an Amazonian agrosilvicultural product (Euterpe oleracea, Mart). Assaí biomass characteristics as fuel were reviewed based on available literature and its availability assessed. The profile of a typical industrial processing unit was described. The electricity generation cost for a 1 MW conversion systems, considering 5.5 US$ t−1 biomass price, were evaluated: conventional steam cycle with backpressure turbine (66.97 US$ MWh−1), steam cycle with extraction condensation turbine (92.11 US$ MWh−1), organic Rankine cycle (ORC – 122 US$ MWh−1) and a gasifier/internal combustion engine set (102 US$ MWh−1). Based on financial performance, back-pressure steam turbine was the best option, and gasifier/internal combustion should be further considered due its operation flexibility. For any system, minimal electricity commercialization price for economical feasibility found was 150 US$ MWh−1.

Dielectric properties and microwave heating of oil palm biomass and biochar

The conversion of the electromagnetic energy into heat depends largely on the dielectric properties of the material being treated. Therefore, the fundamental understanding of these properties is necessary for designing industrial microwave processing unit. The objective of this study is to investigate the dielectric properties of oil palm biomass and biochar at varying frequency in the range 0.2–10GHz. The dielectric properties were measured using a coaxial probe attached to a network analyzer. The results indicate the dielectric constant was found to be inversely proportional to the frequency. However, the biomass in the present study did not obey the famous Debye equation and hence, the loss factor was found to be directly proportional to the frequency. The dielectric properties of oil palm shell (OPS) and its biochar were found to be almost similar and higher than oil palm fiber (OPF). Relaxation time and static dielectric constant were also presented in the paper. Lastly, the heating characteristics under MW irradiation confirmed poor microwave absorbing properties of oil palm biomass.

The High Temperature Annular Shear Cell: A modified ring shear tester to measure the flow properties of powders at high temperature

Although changes of cohesive behaviour of powders is observed at high temperature in several industrial process units, conventional testers and procedures are still not suited for testing powder flow properties at high temperature. In this work a High Temperature Annular Shear Cell was designed, built and set-up in order to directly measure the flow properties of powders up to 500°C. A temperature control system was also developed to establish a uniform temperature inside the powder sample.Yield loci at room temperature and 500°C were measured for samples of fluid catalytic cracking catalyst (FCC powder), fly ashes, natural corundum, synthetic porous α-alumina and glass beads. Experimental evidences did not reveal a univocal effect of temperature in the tested range.Finally, shear tests on glass beads mixed to with some high-density polyethylene (HDPE) (1% of the total weight) confirmed that a significant increase of the cohesive behaviour occurs at high temperature when liquid bridges form due to the melting of one of the solid phases.