Industrial Production Plants Research Articles

Production and export potential of cricket powder in Costa Rica: an exploratory study

Due to its environmental conditions, its efforts towards sustainability and the current cricket rearing activities, the industrial production of edible cricket powder is seen as an economic opportunity for Costa Rica. The feasibility of such a project was explored through a two-stage qualitative research. First, the market needs, product and production requirements were investigated through semi-structured interviews to possible buyers and producers, a visit to an industrial cricket powder production plant and a desk study. During the second stage, the entopreneurial ecosystem in Costa Rica was explored through semi structured interviews to local experts and entopreneurs in order to evaluate the feasibility of complying with the requirements identified in the first stage. Results show that Mexico and the United States are the main potential markets for Costa Rica. The main buyers are food and pet food producers with an average demand of 0.5-2 tons of cricket powder per month. A validated protein content, a gluten-free product and a neutral aroma are key product requirements. For the production, it is required to have an enclosed facility, with temperature and humidity control and a proper food safety and quality management system in place. It is concluded that the current entopreneurial ecosystem in Costa Rica does not allow to meet the international market needs required for commercialising cricket powder. Challenges like the current low production capability, high price, the lack of technical experts and specific regulation on insect production must still be overcome. Though, the main opportunities that would allow such a production are the existent local knowledge on rearing insects, well-established academic, research, and export promotion institutions, governmental small and medium enterprises support programs and the local expertise on exporting butterflies. The creation of an association that defines a national strategy is seen as a necessary step to boost the sector.

Occurrence of Clostridium perfringens vegetative cells and spores throughout an industrial production process of black soldier fly larvae (Hermetia illucens)

The main use of black soldier fly larvae (Hermetia illucens) is currently as an animal feed ingredient. While the bacterial community of the larvae has been characterised repeatedly via sequencing, microbiological safety assessment based on culture-dependent techniques is still scarce. This study focused on the occurrence of the spore-forming foodborne pathogen Clostridium perfringens during rearing and consecutive processing of the larvae, based on observations in a single rearing facility. C. perfringens vegetative cells and spores were determined, in addition to total viable counts, total aerobic spore counts and intrinsic parameters including pH, water activity and moisture content. All samples were obtained from an industrial production plant. In a preliminary experiment, substrate ingredients and dried larvae were analysed, but the larvae were produced with a previous batch of the substrate mixture. A second, more detailed, experiment was performed where all samples were collected sequentially from the same production run (substrate ingredients, substrate mixture, starting larvae, harvested larvae, residue, dried larvae and stored dried larvae). In the two experiments, (presumptive) C. perfringens, as determined on tryptose sulphite cycloserine agar, was found at low numbers in the ingredients and in the second experiment it was also found in the substrate mixture. Over the two experiments, total C. perfringens counts (i.e. vegetative cells plus spores) ranged between 3.0±0.1 and <1.2±0.5 log cfu/g and C. perfringens spores ranged between 2.5±0.1 and <1.0±0.0 log cfu/g. Interestingly, vegetative cells and spores of C. perfringens were below the detection limit in all larvae samples. Therefore, it appears that at this production site and based on the samples investigated, the pathogen did not colonise the larvae. However, these results indicate that insect producers should monitor this pathogen among others, and install good hygiene practices to avoid contamination.

Improved Understanding of Industrial Process Relationships Through Conditional Path Modelling With Process PLS

Understanding how different units of an industrial production plant are operationally related is key to improving production quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts of historical process data. Path modelling is a valuable statistical tool to obtain such information from historical production data. Investigating how relationships within a process are affected by multiple production conditions and their interactions can however provide an even deeper understanding of the plant’s daily operation. We therefore propose conditional path modelling as an approach to obtain such improved understanding, demonstrated for a milk protein powder production plant. For this plant we studied how the relationships between different production units and steps are dependent on factors like production line, different seasons and product quality range. We show how the interaction of such factors can be quantified and interpreted in context of daily plant operation. This analysis revealed an augmented insight into the process that can be readily placed in the context of the plant’s structure and behavior. Such insights can be vital to identify and improve upon shortcomings in current plant-wide monitoring and control routines.

High temperature stability of a commercial terphenyl-based thermal oil

Thermal oils are nowadays largely employed as heat transfer fluids or cooling media in industrial and energy production plants. However, despite their widespread use, very few data are currently available about their chemical stability and possible composition changes in function of the operating temperatures, which are of crucial importance to evaluate the lifetime of these materials in real conditions.In the present work, a commercial terphenyl-based oil was investigated with respect to its thermal stability, performing ageing tests followed by a complete post-characterization analysis.To this purpose, a dedicated experimental set-up was designed and constructed to study the degradation processes, with a qualitatively and quantitatively analysis of the released gases and condensable products, together with a post-ageing characterization of the oil thermo-physical properties.The results show that the main decomposition mechanism involves the loss of hydrogen atoms, followed by partial polymerization processes, which explain the increase in dynamic viscosity and the decrease in volatility observed for the thermally stressed materials. Finally, the decomposition kinetic constants were estimated, obtaining a value of about 100 kJ/mol for the activation energy, which is in good agreement with the data available in the scientific literature.

A pilot-plant for the selective recovery of magnesium and calcium from waste brines

The problem of brines disposal has raised great interest towards new strategies for their valorisation through the recovery of minerals or energy. As an example, the spent brine from ion exchange resins regeneration is often discharged into rivers or lakes, thus impacting on the process sustainability. However, such brines can be effectively reconcentrated, after removal of bivalent cations, and reused for the resins regeneration. This work focuses on developing and testing a pilot plant for selective recovery of magnesium and calcium from spent brines exploiting a novel proprietary crystallization unit. This is part of a larger treatment chain for the complete regeneration of the brine, developed within the EU-funded ZERO BRINE project. The pilot crystallizer was tested with the retentate of the nanofiltration unit processing the spent brine from the industrial water production plant of Evides Industriewater B.V. (Rotterdam, The Netherlands). Magnesium and calcium hydroxide were selectively precipitated by adding alkaline solution in two consecutive steps and controlling reaction pH. Performance was assessed in terms of recovery efficiency and purity of produced crystals, observing in most investigated cases a recovery of about 100% and 97% and a purity above 90% and 96%, for magnesium and calcium hydroxide, respectively.

Detection of Vibrations Defects in Gas Transportation Plant Based on Input / Output Data Analysis: Gas Turbine Investigations

In oil and gas industrial production and transportation plants, gas turbines are considered to be the major pieces of equipment exposed to several unstable phenomena presenting a serious danger to their proper operation and to their exploitation. The main objective of this work is to improve the competitiveness performance of this type of equipment by analyses and control of the dynamic behaviors and to develop a fault monitoring system for the equipment exposed and subject to certain eventual anomalies related to the main components, namely the shaft and the rotors. This study will allow the detection and localization of vibration phenomena in the studied gas turbine based on the input / output data.

Characterization comparison of bottom ash and fly ash during gasification of agricultural residues at an industrial-scale gasification plant – Experiments and analysis

In this study, the characterization of the fly and bottom ashes from an industrial gasification plant for biomass syngas production were investigated and compared in terms of particle size distribution, chemical composition, fusibility, mineralogy and morphology. The experimental results indicated that the characteristic particle sizes of the bottom ashes from gasifier were larger than those of the fly ashes from cyclone or sprayer. By contrast, the fly ashes contained higher contents of potassium, sulphur and chlorine. The base-to-acid ratio (Rb/a) values for those fine fractions in the bottom residues and the fly particles from cyclone were in high deposition range (Rb/a > 1.0) while that for the slags in the bottom ash or the fly particles from sprayer was in medium deposition range (0.5–1.0). As regards the fouling index (Fu), the value of cyclone ash (Fu = 41.2) was higher than those of the other ashes, reflecting its relatively higher deposition tendency on heat transfer surfaces. Besides, the fusion temperatures of the fly ashes were lower than those of bottom ashes. The rounded forms of fly particles could be regarded as the indicative of ash melting. Comparison of thermograms indicated that the bottom ash contained more organics than fly ash, thus explaining its relatively higher weight loss during ash fusion. In practice, more attention should be paid on the rational control of operating temperature, which could relieve the occurrence of ash deposition/agglomeration to some extent.

Treatment of agrochemical wastewater by subcritical water oxidation method: chemical composition and ion analysis of treated and untreated samples

This work aimed at offering an effective and environmentally friendly approach to the real wastewater of an industrial agrochemicals production plant containing high amounts of many hazardous compounds that threaten human health. The removal of total organic carbon (TOC) (59.45%) and colour (97.92%) of the wastewater was achieved using subcritical water oxidation method. The fate of the detected compounds was followed by GC–MS analysis. Several pollutants such as phenol, fumaric acid, chlorpyrifos, penconazole, brassilexin, buprofezin, etoxazole, pyriproxyfen and 2-naphthalene-sulphonic acid which are bio-refractory and harmful to human health, were effectively degraded. Inorganic ions exist in the wastewater or formed through the process and their possible effects on the applied method were analysed. The central composite design was used to optimise the method and fully evaluate the single or combined effects of the method parameters on the removal rates. The precision of the applied design models was evaluated employing ANOVA, Regression coefficients and validation analysis. F and P and R2 values were obtained as 107.43, <0.0001, and 0.9898, respectively in the TOC removal model and 39.45, <0.0001, and 0.9726 in the colour removal model.

Comprehensive evaluation of flue gas desulfurization and denitrification technologies of six typical enterprises in Chengdu, China.

Post-combustion flue gas desulfurization and denitrification technologies are essential in achieving the full compliance of fine particulate matter (PM2.5, aerodynamic diameter less than 2.5 μm) air quality standards by 2030 in China as sulfur dioxide (SO2) and nitrogen oxides (NOX) are the main precursors of PM2.5. Some studies have addressed the performance evaluation of desulfurization technology, but none included the water-soluble ions (sulfate (SO42-), nitrate (NO3-), etc.) as an indicator nor accounted for uncertainty involved. In this study, we present a multilevel fuzzy method that integrates the analytic hierarchy process with fuzzy theory, defines SO42-concentration as a new environmental indicator, and is supplemented with an uncertainly analysis and apply the method for the techno-economic and environmental evaluation of desulfurization and denitrification technologies in six typical enterprises (including two power plants and three industrial production plants and a waste incineration plant) in Chengdu, China. The evaluation shows that first, the fluctuating desulfurization rate and the dosage leads to changed ranking of the economic and technical secondary evaluation results, with the overall comprehensive evaluation ranks unchanged. Second, from the perspective of environmental protection agency and the public, if the environmental indicators are empowered, the lower the SO42-concentration of an enterprise, the better its evaluation ranking will be and vice versa. Third, if we re-empower from the perspective of the enterprise, under the condition that the technical feasibility is met and the environmental indicators are basically up to standard, the low-cost removing process is more likely to be the tendency of the enterprise. In summary, the findings of the study have led to the conclusions that (1) for the power industry, the integration of desulfurization, denitrification, and dedusting technologies should be promoted rigorously; (2) the non-power industry should continue the end-of-pipe treatment and environmental protection regulatory policies of the power industry; and (3) the energy industry structure should be optimized with enhanced end-of-pipe control technologies to achieve deep reduction of air pollutants.

Design, Cost Estimation and Sensitivity Analysis for a Production Process of Activated Carbon from Waste Nutshells by Physical Activation

A conceptual design of an industrial production plant for activated carbon was developed to process 31.25 tons/day of industrial waste nutshells as the raw material and produce 6.6 ton/day of activated carbon using steam as an activation agent. The design considered the cost of the main equipment, the purchase price of the nutshells, basic services, and operation. A sensitivity analysis was developed, considering the price of the finished product and the volume of raw material processing varied up to ±25%. Furthermore, the total annual cost of the product was determined based on the production of 2100 tons/year of activated carbon. Two cash flows were developed and projected to periods of 10 years and 15 years of production, using a tax rate of 27%, a low discount rate (LDR) of 10% per year, and without external financing. For a 10-year production project, the net present value (NPV) was USD 2,785,624, the internal return rate (IRR) 21%, the return on investment (ROI) 25%, and the discounted payback period (DPP) after the fifth year. Considering a project with 15 years of production, the NPV was USD 4,519,482, the IRR at 23%, the ROI 24%, and the DPP after the fifth year of production.

Functional and Social Team Dynamics in Industrial Settings

Like other social systems, corporations comprise networks of individuals that share information and create interdependencies among their actions. The properties of these networks are crucial to a corporation’s success. Understanding how individuals self-organize into teams and how this relates to performance is a challenge for managers and management software developers looking for ways to enhance corporate tasks. In this paper, we analyze functional and social communication networks from industrial production plants and relate their properties to performance. We use internal management software data that reveal aspects of functional and social communications among workers. We found that distinct features of functional and social communication networks emerge. The former are asymmetrical, and the latter are segregated by job title, i.e., executives, managers, supervisors, and operators. We show that performance is negatively correlated with the volume of functional communications but positively correlated with the density of the emerging communication networks. Exposing social dynamics in the workplace matters given the increasing digitization and automation of corporate tasks and managerial processes.

Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems

Advanced technologies (e.g., distributed sensors, RFID, and auto-identification) can gather processing information (e.g., system status, uncertain machine breakdown, and uncertain job demand) accurately and in real-time. By combining this transparent, detailed, and real-time production information with production system physical properties, an intelligent event-driven feedback control can be designed to reschedule the release plan of jobs in real-time without work-in-process (WIP) explosion. This controller should obtain the operational benefits of pull (e.g., Toyota’s Kanban system) and still develop a coherent planning structure (e.g., MRPII). This paper focuses on this purpose by constructing a discrete event-driven model predictive control (e-MPC) for real-time WIP (r-WIP) optimization. The discrete e-MPC addresses three key modelling problems of serial production systems: (1) establish a max-plus linear model to describe dynamic transition behaviors of serial production systems, (2) formulate a model-based event-driven production loss identification method to provide feedback signals for r-WIP optimization, and (3) design a discrete e-MPC to generate the optimal job release plan. Based on a case from an industrial sewing machine production plant, the advantages of the discrete e-MPC are compared with the other two r-WIP control strategies: Kanban and MRPII. The results show that the discrete e-MPC can rapidly and cost-effectively reconfigure production logic. It can decrease the r-WIP without deteriorating system throughput. The proposed e-MPC utilizes the available transparent sensor data to facilitate real-time production decisions. The effort is a step forward in smart manufacturing to achieve improved system responsiveness and efficiency.

A life cycle inventory of microalgae-based biofuels production in an industrial plant concept

Microalgae have been reported as a promising alternative for biofuels production. However, the use of microalgae for biofuels is still a challenge due to the intense energy use and the generation of a significant amount of biomass residues in the process. In order to analyze the environmental impacts of different technological processes for the production of biodiesel from microalgae, several studies have been published making use of the Life Cycle Assessment (LCA) methodology, which allows the recognition of the process bottlenecks and supports the identification of alternatives for a more efficient use of the feedstock. Therefore, in this study, a Life Cycle Inventory (LCI) is compiled, based on real pilot-scale process data, which was scaled-up to a microalgae biomass industrial plant for biofuel production. Values of energy, nutrients, water, and materials consumption are used to create an inventory of inputs and outputs for biomass cultivation and biodiesel production, in order to acquire data to conduct a complete LCA modeling in future studies. According to this model, to produce 1 kg of biodiesel it is necessary about 12 kg of dried algae biomass. This study supports the decision-making process in biofuel production to promote the development of sustainable pilot and large-scale algae-based industry, through the identification of critical factors.

Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems

Software-intensive cyber-physical systems (CPS) perform essential tasks such as controlling automated production processes in industrial production plants. The required levels of autonomy, openness, and self-adaptation, as well as the dynamic nature of the context of such CPS, result in challenging tasks for their engineering. During operation, unexpected situations in which the system has insufficient knowledge about the current state of the system itself as well as its context may occur. Engineering CPS, e.g., for industrial production sites, must account for such uncertainties the system will have to cope with during its lifetime in a structured and systematic way. Since the development of CPS requires consideration of different system perspectives, current uncertainty modeling approaches cannot be applied right away, as they do not explicitly consider uncertainty aspects that affect different artifacts. To aid the engineering of CPS, this article presents a model-based approach to document uncertainty. We propose “Orthogonal Uncertainty Models,” which closely integrate with other engineering artifacts from different perspectives, as a means for capturing a dedicated uncertainty viewpoint. Our approach has been evaluated in the industry automation domain. The application shows that the idea of regarding uncertainty within a dedicated perspective is highly beneficial. Particularly, our approach helps to uncover and document uncertainties related to behavioral, functional, and structural properties of a system, as well as uncertainties related to business models that would otherwise possibly remain covert. Note to Practitioners-Identifying and documenting uncertainties, which may occur during operation of a system, is a common problem in engineering processes. Such uncertainties may lead to severe damage, and thus need to be mitigated appropriately. It is crucial to account for these uncertainties during engineering, especially in the early phases. Depending on the specific project characteristics, a multitude of different diagram types are used to model a system. Uncertainties thus reflect in many artifacts, which leads to: 1) redundancies in the specified uncertainty attached to diagram elements and 2) uncertainty information (e.g., about the cause or effect of uncertainty) that is spread across different diagrams. The latter makes it difficult to structure uncertainty information and trace it throughout the engineering process so that uncertainty can be systematically considered. Our approach provides a graphical modeling language that employs a dedicated perspective on uncertainty in separate diagrams that can be linked to any engineering artifact.

Risk management and distributed simulation in Papyrus tool for decision making in industrial context

Modeling and Simulation (M&S) are important steps in the design of industrial systems and production plants. They help in anticipating and understanding these complex systems in order to make decisions about strategic implementations before effective development. Once the models are satisfyingly built, namely they correctly reflect the system, the simulation can offer many behavioral information to the user. In this paper, we are proposing to integrate risk management, hazard generation, and complexity issues in process modeling with data coming from industrial context: polar power plant design. In this context, risks and hazards must be modeled, but are too large to be in the main model. For this purpose, the paper presents some extensions to the modeling and simulation open source tool: Papyrus. This proposition consists in developing the Papyrus features for outsourcing risks and hazards out of the model, exporting simulation’s data in different components for decision making, and implementing distributed simulation mechanisms for dealing with reusability, and interoperability of components.

Energy performance analysis of continuous processes using surrogate models

Energy intensity is a commonly used key performance indicator (KPI) for the energy performance of production processes and often serves as an Energy Performance Indicator (EnPI). The energy performance of a process depends on a variety of factors like capacity utilization, ambient temperature and operational performance. Understanding the influence of these factors on the relevant KPI or EnPI helps to distinguish between influenceable and non-influenceable contributions and to identify the improvement potential. By describing the best historically observed performance as a function of the non-influenceable factors, valuable information on the efficiency of the current operation of a plant and the improvement potential is provided to plant managers and operators. In this contribution, a method is proposed to identify a surrogate performance model for the attainable energy performance considering the relevant factors. The modeling method is based solely on the evaluation of historical process data and employs a novel combination of known surrogate modeling techniques using clustering, model fitting and model simplification by backward elimination. The method is applied to real process data of a large industrial production plant and the use of the model for process performance monitoring and reporting in accordance with energy management system requirements is illustrated and discussed.

Post-combustion CO2 capture by RVPSA in a large-scale steam reforming plant

Steam reforming (SR) of natural gas is the most widespread process to produce hydrogen. This paper presents a comprehensive simulation of an industrial plant for hydrogen production for the internal use in a refinery industrial complex based on the SR of different feedstocks. The reference plant is a 42,000 Nm3/h hydrogen production unit located in the South of Italy in the Refinery of Milazzo, Sicily. Hydrogen is produced from light gaseous hydrocarbons and purified by means of pressure swing adsorption technology. In the present work, a process simulation has been built on scientific basis and validated against field data at different feedstocks and operating conditions. The model correctly predicts the effect of relevant process parameters such as reformer feed pressure and temperature, steam to carbon ratio and plant load.In addition, the paper proposes the design of an industrial-scale rapid vacuum pressure swing adsorption (RVPSA) unit which was designed and simulated to capture carbon dioxide from the flue gas exiting the steam reformer. The RVPSA unit was integrated in the current plant enabling the concentration of CO2 with a purity of 96.64%, an overall recovery of 90.84% and a specific energy consumption of 628.93 kJ/kgCO2, thus meeting the requirements for transportation and geological storage. An energetic efficiency calculation was introduced to quantify the effectiveness of the hydrogen conversion process and to predict the effect of CO2 capture and some process parameters to the overall H2 production efficiency.

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios.

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

Resource Integration and CO 2 Conversion in Industrial Clusters

Countries around the globe seek ambitious CO2 emission reduction targets to avoid dangerous climate change effects. In this work, a tool was developed that produces a network of plants that can decrease emissions, maximize energy reuse and select products that maximize profit of an industrial cluster. The tool can integrate various plants and/or processes, such as industrial production plants, power plants etc., to enhance the potential of making financial profits. Recently, a focus has been put on integrating natural gas, energy, and CO2 to reduce emissions or energy demand, which fall within end-of-pipe carbon capture utilization and storage (CCUS) solutions. This work designs an industrial cluster on the utilization of key materials including emissions. This approach introduces a new representation of resources management in a cluster that systematically integrates H2O, N2, O2, CO2 and energy (power and heat) converting them to value added products under a reduction target. The approach develops a Linear Program (LP), which can be easily applied to explore different combinations of plants. The method was applied on an illustrative example where several scenarios were investigated. The industrial cluster converts CO2 by 76% and produces methanol, while making profit.

Analyzing the effective length of I cross-section beams in connections with girder clamps for totally removable, reusable and reconfigurable structures

One of the goals of factories in the future and Industry 4.0 is for industrial facilities to be environmentally friendly. This objective should be compatible with saving money, always required of investments in industrial production plants. Industrial structures are part of such investments, used in industrial installations and equipment. In order to achieve the two objectives mentioned above, joints with clamps are a promising solution for the manufacturing of steel structures based on I-section beams. Joints with girder clamps make it possible to build steel structures that are completely removable, reusable and reconfigurable. There is currently a lack of research regarding the minimum length required for beams in joints with clamps according to geometrical measurements of the cross-section of the beam. This work studies the behavior of beams in clamps joints in relation to the effective working length of the beam. This work also verifies whether the use of a fixed angle of stress distribution is suitable for calculating the minimum beam length required for the manufacturing process of this type of structures.