Browsing by Author "Zaman, Prianka Binte"

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Cost reduction through lean approach in natural gas dehydration process: a case study
(2019-03-12) Towfique, Mustafa Shaafee Saleheen; Zaman, Prianka Binte
Lean approaches for cost reduction have been vastly used in batch processing like automobile manufacturing industries (Toyota, Ford), in line manufacturing (Garments, pharmaceuticals industries), discrete manufacturing (aircraft industry) and medical technologies (Oncology). However, this approach has not found to be used in continuous production process like upstream natural gas processing industry. For this research, a total number of 03 (three) cases were modeled, incorporating change in a natural gas plant’s Charcoal filter consumption, varying absorbent (Tri-ethylene glycol) flow rate and tuning Re-boiler still column temperature. In this study, a lean six sigma approach has been applied in country’s largest natural gas plant’s gas dehydration process by using different tools like DMAIC model (Define, Measure, Analyze, Improve & Control), BOAT model (Barrier, Opportunity, Action & Threat), COPQ (Cost of Poor Quality) calculation, PICK chart (Possible, Implement, Challenge and Kill). Applying these tools, absorbent (TEG) loss and carbon filter replacement frequency has been reduced in atmospheric condition of Bangladesh and operational cost reduced to AFB $3,63,365 approximately. From this study; other natural gas plants of Bangladesh as well as of Asia, Africa and Arab basins can gain an understanding of lean six sigma process implementation and practices of sustainable cost reduction.
Exprerimental and numerical investigations of minimum quality lubrication with hybrid nano-fluid on machinability of titanium alloy
(Department of Industrial Production and Engineering, 2021-03-09) Zaman, Prianka Binte; Dhar, Dr. Nikhil Ranjan
Titanium alloy, particularly Ti-6Al-4V, is extensively utilized in versatile engineering applications such as aerospace, automobile, biomedical, chemical and other manufacturing industries owing to their enviable and inimitable mechanical properties. But their machinability is usually considered to be poor because of low thermal conductivity, low elastic modulus, high hardness and high chemical reactivity. In the area of manufacturing, any attempt to improve the machinability of such hard-to-machine material is encouraged. Application of cutting fluids can reduce the cutting temperature and friction which heading to prolonged tool life and improved machining performance. But, conventional cutting fluids application techniques are known for being expensive, polluting and a non-sustainable part of modern manufacturing processes. Manufacturing industries are now being forced to implement economical, ecological and sustainable cooling strategies. Minimum Quantity Lubrication (MQL) technique is a modern technique, where a very small amount of coolant/ lubricant is delivered to the cutting zone as a form of a mist which can improve the machinability. However, the performance of MQL should be enhanced when utilized for machining hard-to-machine materials. Optimum selection of MQL delivery system parameters and improved cutting fluid can enhance its performance. Nano-fluid which comprises superior tribological and thermo-physical properties is a promising alternative to MQL fluid. This fluid can effectively improve the cooling and lubrication efficiency of MQL. Moreover, hybrid nanofluid can be a better choice due to its synergistic effects of integrating multiple nanoparticles. Optimization of process parameters, nano-fluid concentration and tool type also helps to achieve the ultimate machining performance. In this study, a double jet MQL delivery system has been designed and fabricated for delivering mist directly towards the chip-tool and work-tool interfaces in turning Ti-6Al-4V alloy. Hybrid nano-fluid has also been prepared and applied through the MQL technique. The performance of the newly designed double jet MQL system and hybrid nano-fluid-based MQL has been systematically investigated. Double jet MQL combined with hybrid nano-fluid has performed the best compared to dry, single jet MQL with conventional cutting fluid and double jet MQL with conventional cutting fluid. Finally, process parameters have been optimized due to achieve an overall efficient manufacturing system for turning Ti-6Al-4V alloy. This thesis also presents a finite element model of turning Ti-6Al-4V alloy by carbide insert. Based on this model the effects of hybrid nano-fluid-based MQL application on temperature distribution and chip formation were investigated in simulations. The chip-tool interface temperature distribution and chip morphology in simulation show a good agreement with the measured value. This study revealed the promising behavior of nano-fluid-based double jet MQL in turning Ti-6Al-4V alloy by coated and uncoated carbide inserts within a specified range of cutting parameters. Nano-fluid-based MQL has an enormous opportunity to enhance the machinability of Ti-6Al-4V alloy considering environmental issues and should be explored further.
Market Sales Prediction by Analyzing Customer Buying Patterns Using Machine Learning
(©Daffodil International University, 2021-12-17) Himi, Tamanna Rahman; Zaman, Prianka Binte; Al-Amin, MD.
To improve a business, a company has to analyze the type of purchases they must keep track of the merchandise that are marketing the foremost in order that they will keep stock of these class merchandise and take away those forms of class that are marketing less. ‘Sales’ is the crucial success issue of a business. Increasing sales may be an excellent impact factor for a developing business. During this trendy time, it may be done by victimizing trendy technology like AI, machine learning, and deep learning. So, we are needing to do that job victimization machine learning by utilizing algorithms. In our research we have a tendency to act on however a mercantile establishment will get a lot of sales from its product victimization of its customer’s previous product shopping for information. We've to preprocess victimization using totally different pre-processing techniques. Information exploration, data transformation and engineering play an important role in predicting correct results. This paper discusses a way to predict sales maximization by information analysis and the way to evaluate the effectiveness of machine learning techniques. Sales analysis of products is one of the major issues of identification buying frequency pattern. We proposed a model to predict seasonal products which is the “ARIMA” model. This model works to do time series analysis. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Our recommended models can be used to get an idea of which products need to be kept on a shop’s shelves and which products are not for the advantage of the customer. Based on the customer's purchases for a few years this model will be able to recommend which products are more popular in which season.