Cost Production Research Articles

Novel curled cooler to enhance the efficiency of a photovoltaic panel system

The overheating of photovoltaic panels can result in decreased efficiency, potential long-term damage, and reduced energy production. This study proposes a passive cooling system with a new curled aluminum fin profile to mitigate these issues. The thermal response of solar panels with variations in the geometrical parameters of the new curled fin design was studied using computational fluid dynamics (CFD) simulations, where the best-curled fin design option was selected for its implementation. Experimental tests were conducted to determine the thermal-energy response in a PV panel with the new design, compared with a rectangular fin design and without a cooling system as a reference. An economic analysis was also conducted to quantify the payback time based on the energy cost and surplus production rate. The experimental results showed that the new proposed system reduces the panel temperature to 8 °C, knowing that the no-fins system increases the PV panel temperature to 16 °C above ambient. Additionally, the energy production was increased by 4 % with the proposed curled fins, and the payback time varied between 1.1 to 6.4 years, depending on the kWh energy cost per country.

Developing an Adaptive Neuro-Fuzzy Inference System for Performance Evaluation of Pavement Construction Projects

This study employs an adaptive neuro-fuzzy inference system (ANFIS) to identify critical success factors (CSFs) crucial for the success of pavement construction projects. Challenges such as construction cost delays, budget overruns, disputes, claims, and productivity losses underscore the need for effective project management in pavement projects. In contemporary construction management, additional performance criteria play a vital role in influencing the performance and success of pavement projects during construction operations. This research contributes to the existing body of knowledge by comprehensively identifying a multidimensional set of critical success performance factors that impact pavement and utility project management. A rigorous literature review and consultations with pavement experts identified sixty CSFs, categorized into seven groups. The relative importance of each element and group is determined through the input of 287 pavement construction specialists who participated in an online questionnaire. Subsequently, the collected data undergo thorough checks for normality, dependability, and independence before undergoing analysis using the relative importance index (RII). An ANFIS is developed to quantitatively model critical success factors and assess the implementation performance of construction operations management (COM) in the construction industry, considering aspects such as clustering input/output datasets, fuzziness degree, and optimizing five Gaussian membership functions. The study confirms the significance of three primary CSFs (financial, bureaucratic, and governmental) and communication-related variables through a qualitative structural and behavioral validation process, specifically k-fold cross-validation. The outcomes of this research hold practical implications for the management and assessment of overall performance indices in pavement construction projects. The ANFIS model, validated through robust testing methodologies, provides a valuable tool for industry professionals seeking to enhance the success and efficiency of pavement construction endeavors.

Strategi Perusahaan Maskapai Penerbangan dalam Menghadapi Pandemi Covid-19: Pendekatan Akuntansi Manajemen

The Covid-19 pandemic has caused many companies to experience a decline in revenue and profits, including airline companies. PT Garuda Indonesia is one of the companies included in the tourism industry ecosystem which has also been affected by the Covid-19 pandemic. This research aims to investigate the strategies companies choose to face the Covid-19 pandemic. This research uses a qualitative method with a case study approach. The research results show that cost efficiency and product differentiation strategies are able to increase company revenues.

Catastrophic costs for tuberculosis patients in India: Impact of methodological choices.

As financial risk protection is one of the goals towards universal health coverage, detailed information on costs, catastrophic costs and other economic consequences related to any disease are required for designing social protection measures. End Tuberculosis (TB) Strategy set a milestone of achieving zero catastrophic cost by 2020. However, a recent literature review noted that 7%-32% TB affected households in India faced catastrophic cost. Studies included in the review were small scale cross-sectional. We followed a cohort of 1482 notified drug-susceptible TB patients from four states in India: Assam, Maharashtra, Tamil Nadu, and West Bengal to provide a comprehensive picture of economic burden associated with TB treatment. Treatment cost was calculated using World Health Organization guidelines on TB patient cost survey and both human capital and output approaches of indirect cost (time, productivity, and income loss related to an episode) calculation were used to provide the range of households currently facing catastrophic cost. Depending on choice of indirect cost calculation method, 30%-61% study participants faced catastrophic cost. For over half of them, costs became catastrophic even before starting TB treatment as there was average 7-9 weeks delay from symptom onset to treatment initiation which was double the generally accepted delay of 4 weeks. During that period, they made average 8-11 visits to different providers and spent money on consultations, drugs, tests, and travel. Following the largest cohort of drug-susceptible TB patients till date, the study concluded that a significant proportion of study participants faced catastrophic cost and the proportion was much higher when income loss was considered as indirect cost calculation method. Therefore, ensuring uninterrupted livelihood during TB treatment is an absolute necessity. Another reason of high cost was the delay in diagnosis and costs incurred during pre-diagnosis period. This delay and consequently, economic burden, can be reduced by both supply side (intense private sector engagement, rapid diagnosis) and demand side (community engagement) initiatives. Reimbursement of expenses incurred before treatment initiation could be used as short-term measure for mitigating financial hardship.

Influence of feedstock selection on cellulosic ethanol production based on densified biomass with calcium hydroxide and regular steam pretreatment

Feedstock selection is a key factor affecting the process of cellulosic ethanol production. Densifying lignocellulosic biomass with chemicals (calcium hydroxide) followed by regular steam autoclave (DLCA(ch)) is an emerging and efficient pretreatment technology. In this study, different crop straws were selected as feedstock in a DLCA(ch) based process for cellulosic ethanol production, and their experimental and economic performances were evaluated. Both single crop straws and mixed crop straws were used and compared as feedstock for DLCA(ch) based biorefinery. Among single crop straws of corn stover, rice straw and wheat straw, wheat straw had the highest ethanol yield and the lowest feedstock cost, with a minimum ethanol selling price of 2.03 $/gal ethanol considering a 10 % internal rate of return and a 2000 tons crop straw per day biorefinery scale. When mixed crop straws were used as the feedstock, both feedstock cost and ethanol production cost were further reduced, and the minimum ethanol selling price was reduced to 2.02 $/gal ethanol. In addition, the use of mixed crop straws has great potential for large scale DLCA(ch) based process for cellulosic ethanol production.

Reward-Based Crowdfunding Versus Initial Coin Offerings

Inspired by emerging financing techniques that offer additional financing options for entrepreneurs, we establish a stylized model to compare two prevalent crowdfunding strategies: Reward-based crowdfunding and Initial Coin Offerings (ICOs). First, we highlight that the fundamental difference between reward-based crowdfunding and ICO lies in the distinct types of price distortion, resulting in different agency costs. Specifically, compared to the first-best benchmark, reward-based crowdfunding sets a lower funding price to attract more consumers to pre-order, while ICO sets a higher market price due to speculators’ revenue-sharing in the market stage. Second, we show that the financing limit (i.e., the maximum funds raised) of reward-based crowdfunding is driven by the platform traffic, whereas that of ICO is driven by the marginal production cost. Therefore, even with numerous speculators, ICO may result in a lower financing limit than reward-based crowdfunding when the platform traffic or the marginal production cost is high. Third, we explore how products’ cost structures shape entrepreneurs’ financing preferences. ICO suits intangible products with high fixed cost and low marginal production cost, while reward-based crowdfunding aligns better with products exhibiting the opposite cost structure. Finally, we extend our model to show the robustness of our findings and offer some additional insights. For example, ICO may amplify the risk of market uncertainty compared to reward-based crowdfunding; Security Token Offerings (STO) benefits from the absence of agency costs between the entrepreneur and speculators compared to ICO while it may suffer from equity loss, particularly in risky and regulated markets, etc.

High-efficiency thermal reduction of CO2 to high-valued carbon nanotubes

To reduce the amount of carbon dioxide (CO2) already emitted into the atmosphere, various carbon capture, utilization, and storage (CCUS) technologies have been developed for climate and economic reasons in recent years. However, the state of art CCUS techniques suffer from deficiencies such as high cost, low yield, and harsh and discontinuous production conditions. In this work, we report a new thermal reduction method for continuously conversing CO2 to methane (CH4) by introducing sodium borohydride (NaBH4) as a reductant under catalyst-free conditions. Experimental results show that CO2 can be fully captured and converted to CH4 and carbon monoxide (CO) and the selectivity efficiency of CO2 to CH4 can be achieved to be as high as 90% with the optimization of experimental conditions. Such a result originates from the high activity of NaBH4 and high selectivity of the thermal reduction of CO2. To demonstrate one of the potential usages of the reduction products, we integrated the thermal reduction of CO2 with the production of carbon nanotubes (CNTs). As long as CO2 is supplied at the source end, the reduction products can undergo catalytic decomposition to produce a CNT fiber or film continuously at the final end. The present technique is featured with simple equipment setup, low cost, and continuous thermal reduction, thus providing a new pathway for the large-scale conversion of CO2 into high-valued products for wide applications.

Novel energy-efficient design of side-stream extractive distillation process with intermediate reboiler for separating multi-azeotropic mixture based on multi-objective optimization

Side-stream extractive distillation is extensively employed to separate azeotropic mixtures since its energy-efficient benefits. Nevertheless, its economic effect is not always obvious due to the introduction of expensive high-pressure steam. In this article, a conventional triple-column extractive distillation process is firstly researched for separating n-hexane/ethyl acetate/acetonitrile mixture. To effectively decrease the requirement for high-pressure steam and achieve a reduction in economic costs of side-stream extractive distillation, three innovative side-stream extractive distillation processes with intermediate reboiler are proposed. A multi-objective genetic method is applied to optimize the four processes with total annual cost (TAC) and entropy production as two objective functions. Additionally, due to the presence of intermediate reboiler, heat integration design is implemented to the three side-stream extractive distillation processes with intermediate reboiler. Compared to the triple-column extractive distillation process, the heat integration of the three side-stream extractive distillation processes with intermediate reboiler exhibit better performance considering economy, energy-efficient, and entropy production. It indicates that the heat integration of the double side-stream extractive distillation process with intermediate reboiler can reduce 20.10% of TAC, 29.65% of energy consumption, and 35.63% of entropy production, respectively.

Investigation of side-stream extractive distillation for separating multi-azeotropes mixture based on multi-objective optimization

In this study, a conventional triple-column extractive distillation process and three side-stream extractive distillation processes are investigated for separating a ternary mixture of n-hexane/ethyl acetate/acetonitrile. A multi-objective genetic optimization method is applied to optimize the four processes with the minimum total annual cost (TAC) and entropy production as two objective functions. The results indicate that the three side-stream extractive distillation processes can effectively reduce the remixing phenomenon, achieving a reduction in energy consumption. In addition, the three side-stream extractive distillation processes exhibit better economic advantages and significantly decrease entropy production compared to the conventional triple-column extractive distillation process. Among the processes, the single side-stream extractive distillation process presents the optimal performance considering economy, energy-efficient and entropy production. Specifically, it can reduce 6.17% of TAC, 7.85% of energy consumption and 15.54% of entropy production in comparison to the conventional triple-column extractive distillation process, respectively.

Optimizing CO2 Adsorption/Desorption via the Coupling of Imidazole and Carbon Nanotubes Paper for Spontaneous CO2 Uptake from Ambient Air and Solar‐Driven Release

AbstractDirect air capture (DAC) is a sustainable technology to alleviate the greenhouse effect and a reliable pathway to acquire inexhaustible CO2 for the production of costly chemicals and energy products. Current DAC technologies with amine‐related sorbents rely on chemisorption, while they consume intensive energy for CO2 release and sorbent regeneration by heating. Developing new DAC processes with weak, reversible adsorption can substantially reduce the regeneration energies. Herein, the design of CO2 breathing paper (CBP) is demonstrated toward spontaneous CO2 extraction from ambient air and solar‐driven regeneration. The CBP is fabricated by coupling 2‐ethyl‐4‐methylimidazole to carbon nanotube paper on the basis of density functional theory calculations. At ambient conditions, CBP spontaneously captures atmospheric CO2 with a capacity of 0.14–1.75 mmol g–1 at 0–35 °C through non‐covalent electrostatic interaction. Upon exposure to sunlight, all adsorbed CO2 can be released and converted to concentrated gas for storage. Attractively, the efficiency of solar‐driven CO2 release is much higher than the traditional temperature‐swing method owing to the IR sensitivity of CO2. Besides the reversibility, the mild conditions also ensure the durability of CBP. These findings suggest that the CBP is a promising candidate for cost‐effective DAC.

Monitoring Indian “Superfood” Moringa oleifera Lam. – species-specific PCR-fingerprint-based authentication for more consumer safety

Moringa oleifera Lam. has become one of the major new superfoods commonly available in the aisles of bio-shops and health-food sections in supermarkets of North America and Europe. While most of these products appear under the generic and scientifically inconclusive term “Moringa”, the European Union, so far, has allowed commercialisation for the use in food and feed for M. oleifera only. M. oleifera is indigenous to India and South Asia, but large-scale cultivation of this species has spread to the tropical regions on all continents, with a strong focus on Africa, leading to a high risk of admixture with species like M. stenopetala (Baker f.) Cufod. that is native to Africa. In the present study, we have characterised six species of Moringa in order to develop a simple and robust authentication method for commercial products. While the plants can be discriminated based on the pinnation of the leaves, this does not work for processed samples. As alternative, we use the plastidic markers psbA-trnH igs and ycf1b to discern different species of Moringa and develop a diagnostic duplex-PCR that clearly differentiates M. oleifera from other Moringa species. This DNA-based diagnostic assay that does not rely on sequencing was validated with commercial products of “Moringa” (including teas, powders, or capsules). Our method provides a robust assay to detect adulterations, which are economically profitable for costly superfood products such as “Moringa”.

Aligning cooperative supply chain relationships with firm strategy: a cross-disciplinary analysis

Purpose This study aims to investigate the conflicting views in supply chain and strategic management literature regarding cooperative supply chain relationships (CSCR) and firm performance. Supply chain literature suggests a universally positive impact of CSCR on performance, irrespective of a firm’s strategy. In contrast, strategic management literature contends that the effectiveness of CSCR depends on their alignment with the firm’s competitive strategy. The research aims to clarify this disparity, offering insights into the strategic use of CSCR for enhancing firm performance. Design/methodology/approach This paper theorizes the integration of perspectives for the impact of CSCR on firm performance by examining the relationships considering the alignment of cost leadership and product differentiation strategies with supplier and customer relationships. Plant-level survey data is analyzed using regression techniques to test four hypotheses. Findings All four main relationships (cost leadership, product differentiation, supplier relationship and customer relationship) on firm performance are statistically significant. However, cost leadership firms are better aligned to their chosen strategy when they have strong relationships with suppliers, whereas similar relationships with customers create misalignment, negatively influencing firm performance. In contrast, product differentiators benefit by investing in relationships with customers rather than with suppliers. Practical implications A firm’s performance does not solely depend on its CSCR efforts but on aligning them with the firm’s overall strategy. Therefore, managers need to be cognizant of the firm’s competitive strategy when investing in CSCR. Failing to do so could negatively impact firm performance and, eventually, its ability to compete in the marketplace. Originality/value Scholars have advocated for the importance of examining competing perspectives of phenomena, both within and across various bodies of literature, as cross-disciplinary analysis often brings enhanced focus and depth, leading to improved understanding. This research is one of the initial efforts to empirically analyze the varying perspectives on CSCR in supply chain and strategic management literature. This cross-disciplinary approach can yield a more integrated perspective.

Leveraging On Corporate Governance For Supply Chain Resilience Of Sugar Manufacturing Firms In Kenya

Sugar manufacturing firms in Kenya have faced a myriad of challenges with regard to cost control, production inefficiencies, incompetent management and lack of information technology integration in production issues which are directly controlled by management.  The influence of corporate governance on supply chain resilience of sugar manufacturing firms in Kenya thus cannot be underrated.  The specific objective of the study was; to assess the effect of Corporate Governance on Supply chain Resilience of Sugar Manufacturing firms in Kenya. The research study methodology followed a descriptive approach with a target population of the study of 240 respondents, covering various departments within the Sugar firms. A census survey was conducted on all the 15 registered sugar manufacturing firms in Kenya. Data collected was analyzed by both descriptive and inferential statistics using statistical package for social sciences (SPSS Version 28). Descriptive statistics involved calculation of means, frequencies, percentages and standard deviation. Inferential statistics on the other hand included the use of Pearson correlation coefficient to determine the extent of relationship among the independent study variables, while multiple regression analysis was used to establish the relationship between corporate governance and supply chain resilience. Findings from data analysis in the study indicated that corporate governance was significantly responsible for the supply chain resilience of sugar manufacturing firms in Kenya. Consequently, yielding a positive relationship in the regression model. The findings from the regression model indicated that corporate governance, significantly and positively affected supply chain resilience of sugar manufacturing firms in Kenya. The study therefore recommended that the management of sugar manufacturing firms in Kenya in collaboration with the appointing authorities should embrace good practices when appointing managing directors in-charge of the sugar firms to achieve supply chain resilience through, continuous production, resource optimization, quality output and general bottom line improvement.

Programmable DNA Interphase Layers for High-Performance Anode-Free Lithium Metal Batteries.

Anode-free lithium (Li) metal batteries are promising candidates for advanced energy storage, attributed to their appealing characteristics such as high energy density, low cost, and convenient production. However, their major challenges lie in the poor cycling and rate performance owing to the inferior reversibility and kinetics of Li plating and stripping, which significantly hinder their real-world applications. Here, it is demonstrated that deoxyribonucleic acid (DNA), the most important genetic material in nature, can serve as a highly programmable interphase layer for innovation of anode-free Li metal batteries. It is found that the abundant base pairs in DNA can contribute transient Li-N bonds that facilitate homogeneous Li+ flux, thus resulting in excellent Li plating/stripping kinetics and reversibility even at a harsh areal current of 15 mA cm-2. The anode-free LiFePO4 full batteries based on an ultrathin (0.12 µm) and ultralight (≈0.01 mg cm-2) DNA interphase layer show high CEs (≈99.1%) over 400 cycles, corresponding to an increase of ≈186% compared with bare copper (Cu) foil. These results shed light on the excellent programmability of DNA as a new family of interphase materials for anode-free batteries, and provide a new paradigm for future battery innovation toward high programmability, high sustainability, and remarkable electrochemical performance.

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.

ASSESSING THE ADOPTION OF FRUGAL INNOVATION USING SEM FROM CUSTOMERS’ AND FIRM’S PERSPECTIVES

This study assesses the adoption of frugal innovation from customers’ and firms’ perspectives using Structural Equation Modelling (SEM). Three measures of frugal innovation were considered including cost reduction, core functionalities, and performance level. Then, measurement and structural models were developed with three latent, frugal innovations, firm performance, and customer acceptance. Data were collected from 40 Jordanian firms and 200 customers. In total, 300 surveys were distributed. The response rate was 75%. Results indicated that cost reduction, core functionalities, and product performance were mostly affected by used material, functional aspects, and quality of innovative products, respectively. Further, revenues and meeting requirements had the largest impacts on firm performance and customer acceptance, respectively. Finally, frugal innovation significantly impacts firm performance directly and indirectly through customer acceptance. However, the impacts of customer acceptance on firm performance were found insignificant. In conclusion, frugal innovation can enhance firms’ performance and hence firms should offer customers affordable and good enough low-cost, simple, and robust innovative products to have a successful adoption of frugal innovation.

ІНСТРУМЕНТИ ПРИЙНЯТТЯ СТРАТЕГІЧНИХ РІШЕНЬ В УПРАВЛІННІ ВИТРАТАМИ ПІДПРИЄМСТВА

Traditional cost accounting is aimed at identifying costs that are incurred on an ongoing basis in connection with operating activities. The concept that supports the decision-making process in the long term is strategic cost management. The purpose of the article is to identify the tools for strategic decision-making in cost management focused on the corporate strategy of an enterprise.
 It is determined that strategic cost management is a process of making decisions on specific cost drivers in the context of the company's development strategy and value chain. An enterprise should evaluate its value chain in comparison with similar chains of competitors. The article presents a methodology for such an assessment.
 It is proved that strategic management is based on the following assumptions: connection with the value chain, strategic positioning of the enterprise, analysis of cost drivers, the client is the starting point for decision-making and activities, which results in the adoption of the market price as the basis for cost planning and product development, implementation of cost management in the early stages of the product life cycle, since the cost is largely determined by the product features determined at the stage of its design and development. A key element of targeted analysis is cost estimation in terms of the impact of specific factors (drivers). Strategic cost drivers are determined based on the implementation of the development strategy and its connection with the value chain. There are two main types of them: structural cost drivers and implementation cost drivers.
 It is substantiated that strategic cost management involves the use of product life cycle costing. Depending on the phases of the product life cycle, various management methods based on basic models are used: "ABC-Costing, Target-Costing. Strategic positioning of the company in terms of costs incurred should be carried out by means of cost benchmarking, which is aimed at comparing the costs incurred by the company and its competitors. The subject of comparisons is the cost of processes that allow achieving a competitive advantage.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Pengurangan Impor Sodium Klorat di Indonesia melalui Desain ClO2 Semi-Integrated pada Modifikasi ClO2 R8 Plant 10 Ton/Hari

Pulp production always has a bleaching process that uses chlorine dioxide (ClO2) as a bleaching pulp agent. One of the ClO2 production units that are used in the Pulp Industry in Indonesia is a ClO2 R8 non-integrated plant with the process of reacting sodium chlorate, sulfuric acid, and methanol. This ClO2 production type has a weakness, especially in supplying the raw material sodium chlorate, which is needed to import, and the characteristics are easy to explode, making production costs high. This research aims to modify the ClO2 R8 non-integrated by changing to a ClO2 semi-integrated plant. In this research, HYSYS was used to simulation to get mass and energy balance and other component data, and the design of equipment based on the American Society of Mechanical Engineers (ASME), Kern, internal data company, and others to get the specification equipment and also drawn using Autocad. The last step is to account for the economic for getting cost of production before and after modification, fixed capital cost, benefit, payback period, and return on investment. The resulting area, the first, modification of the ClO2 R8 plant by changing to ClO2 semi-integrated could produce chlorate solution 510 g/l which they use electrolyzers system from Chemetics with 20 cell M25 and hypo system. Second, the cost production for the ClO2 semi-integrated plant is 1393 USD/T, the benefit is 599.79 USD/year, the Payback period (PBP) is 6,65 years with an investment of about 3988 KUSD and RoI 15%.

Detection of artichoke on seedling based on YOLOV5 model

Robotic systems have become essential in the industrial field today. Robotic systems used in many areas of industry enable the development of mechanization of agriculture. Researches in recent years have focused on the introduction of automatic systems and robot prototypes in the field of agriculture in order to reduce production costs. The developed smart harvest robots are systems that can work uninterrupted for hours and guarantee minimum cost and high production. The main element of these systems is the determination of the location of the product to be harvested by image processing. In addition to the programs used for image processing, deep learning models have become popular today. Deep learning techniques offer high accuracy in analyzing and processing agricultural data. Due to this feature, the use of deep learning techniques in agriculture is becoming increasingly widespread. During the harvest of the artichoke, its head should generally be cut off with one or two leaves. One main head and usually two side heads occur from one shoot. Harvest maturity degree is the time when the heads reach 2/3 of their size, depending on the variety character. In this study, classification was made by using the deep learning method, considering the head size of the fruit. YOLOv5 (nano-small-medium and large models) was used for the deep learning method. All metric values ​​of the models were examined. It was observed that the most successful model was the model trained with the YOLOv5n algorithm, 640x640 sized images with 20 Batch, 90 Epoch. Model values ​​results were examined as “metrics/precision”, “metrics/recall”, “metrics/mAP_0.5” and “metrics/mAP_0.5:0.95”. These are key metrics that measure the detection success of a model and indicate the performance of the relevant model on the validation dataset. It was determined that the metric data of the “YOLOv5 nano” model was higher compared to other models. The measured value was Model 1= Size: 640x640, Batch: 20, Epoch: 90, Algorithm: YOLOv5n. Hence, it was understood that “Model 1” was the best detection model to be used in separating artichokes from branches in robotic artichoke harvesting.