Artificial Neural Networks (ANNs) Experiment

Artificial Neural Networks (ANNs) Experiment Abstract Artificial neural networks (ANNs) is applied for prediction of product selectivity in biological sulfide removal. An experimental setup is constructed for investigation of biological sulfide removal in a fed batch bioreactor. In this process, sulfide is biologically converted into elemental sulfur and sulfate by the bacterium Thiobacillus thioparus. In addition, thiosulfate is produced in a side reaction. The effect of various parameters (dissolved oxygen, concentration of bacteria and sulfide load) is investigated on the types of products. The main product is elemental sulfur at low dissolved oxygen or at high sulfide loads and also more sulfates are produced at high dissolved oxygen. At high concentration of bacteria, sulfur and sulfate selectivity are increased, and thiosulfate selectivity is decreased. By using gathered experimental data, an artificial neural network model is developed to calculate the selectivity of products at different operating conditions. The comparison betw een proposed ANN model and the experimental data demonstrates a great precision of the model. Introduction Hydrogen sulfide as an extremely toxic gas is emitted by many industries such as oil, gas and petrochemical industries [1]. It has potential for the damaging nervous system at low-dose exposures. Furthermore, sulfide is highly corrosive and has a very unpleasant odor. The threshold limit value for air 0.5–10 ppbv [2], natural gas 4 ppmv [3] and for fresh or salty water fish is 0.5 ppm [4]. In the recent years, biological sulfide removal at ambient temperature and pressure has been investigated as an alternative to the conventional methods. A review on the bacteria of the sulfur cycle was discussed by Tang et al which contributes to a better understanding of the process [7]. Also, a review of the biological removal of H2S from gas streams was studied by Sayed et al [8]. Several microorganisms, namely sulfur compound oxidizing bacteria (SOB), are capable of oxidizing H2S at ambient temperatures and pressures. Different types of bioreactors are used for biological sulfide removal, the more common types are: bioscrubber, biotrickling filter, and biofilter. In the last two processes, the H2S-containing gas passes through a moist, packed bed of particles, which are coated by microorganisms. The biotrickling filter, and biofilter are proper for low sulfide capacity, which the sulfide is mainly converted to sulfate [8]. In the case of biological H2S removal from natural gas, the bioscrubber is more preferred. In a bioscrubber, H2S is washed from the natural gas stream by an alkaline such as NaOH (Eqs. 1 and 2) in a gas absorber, then the rich alkaline solution is sent to an agitated bioreactor where the sulfide ions (HS) are converted to elemental sulfur or sulfate (Eqs. 3 and 4). The produced elemental sulfur is separated by sedimentation [8-11]. Production of elemental sulfur is preferred since it is less harmful than sulfate. Furthermore, hydroxyl ions, consumed in the absorption of H2S in the alkaline liquid, are regenerated upon oxidation of sulfide to elemental sulfur (Eq. 3). Also, elemental sulfur is easily separated by sedimentation. In addition to the biological reactions, dissolved sulfide can react with S0 to produce polysulfide ions (), according to Eq. 5, and ions are abiotically oxidized to S0 and, according to Eq. 6 [12]. Teder [13] has shown that the chain length of polysulfide ion (Sx2-) increases with increase of temperature (x = 5.0 at 25  °C and x = 6.5 at 80  °C). At moderate alkaline conditions, the average chain length of polysulfide (x) varies from 4.6 to 5.5 [12-15]. The product selectivity in the biological sulfide removal process depends on different parameters such as bacteria concentration, sulfide load to the bioreactor and dissolved oxygen [*** Roosta]. This study investigates the applicability of artificial neural networks (ANNs) for the prediction of the biological sulfide removal performance in a fed batch bioreactor. Materials and Methods In this study, Thiobacillus thioparus (DSMZ 5368) was used as sulfur oxidizing bacteria for producing elemental sulfur in the biological sulfide removal process.In this regards, a bioreactor with total volume of 3.8 liter operated under fed batch conditions was used, as shown in Figure (1). During all experiments, the temperature was controlled at 30 ±0.3  °C, and the pH was controlled at 8 ±0.2 using 1N HCl and 1N NaOH solutions. Dissolved oxygen (DO) as a manipulated variable was changed between 0.5 and 6 ppm and controlled using nitrogen and oxygen injection. The bioreactor was charged with 2000 cc of the medium inoculated with biomass. After stabilizing of the temperature in the bioreactor at 30  °C, feeding of the sulfide solution was started. The concentration of sulfide was determined based on the methylene blue method proposed by Trà ¼per and Schlegel [16], by using a spectrophotometer (Zeiss) at 665 nm. The sulfide solution was injected to the bioreactor by an infus ion pump (JMS OT-701), after calibration of the pump. During the experiments, the sulfide solution is applied at different flow rates (between 1.5 and 23 ml h-1) to achieve different HS load (between 0.5 and 4.0 mmolL-1h-1). The flow rate of recirculating gas was adjusted to 15 L min-1 and was spread by a diffuser; this caused a good mixing of the broth. The concentration of sulfur compounds were measured during the process. In this regards, the total concentrations of sulfide (HS and polysulfide) were determined based on the methylene blue method [17]. The concentration of polysulfide was determined based on Teder [14] method. The concentration of sulfide (HS) is the difference between total sulfide concentrations and polysulfide concentration. The concentration of sulfate was determined via the turbiditimetry method at 420 nm [17], and the thiosulfate concentration was determined via the methylene blue method at 760 nm [18]. Finally, the concentration of elemental sulfur is calculated by the mass balance on sulfur. The present study investigates the effect of operating variables: dissolve oxygen (DO) value (0.5 – 4 ppm), HS load (0.5 – 4 mmol L-1 h-1) and optical density (OD) of bacteria (0.4 – 0.6) on the sulfide removal and product selectivity. ANN Artificial Neural Networks (ANNs) with different structures has been proven to be universal function approximators. The major advantage of ANN model is to be constructed without detailed information about the underlying process. ANNs as black box modeling tools have already been used for many applications in industry, business and science [19]. Since in white box modeling approaches, the model of development is based on the information of mechanistic and relevant equations and detailed knowledge for a specific system is usually not directly available, most efforts in the white box modeling approach are devoted to revealing all relevant mechanisms and quantifying these mechanisms correctly. This usually requires an extensive research program (including experiments, which can also be very time- and money-consuming). Here a compromise must be made in order to save time and money. Therefore, white box models often have limited accuracy, because in developing the models, minor mechanisms are neglected and only the major mechanisms are taken into account. The major advantage of the artificial neural networks is that they can be constructed without the need of detailed knowledge of the underlying system. One of the applications of artificial neural network models is to map an input space to an output space and function as a look-up table. Thus, in recent years, artificial neural networks have been applied to biotechnology and biochemical engineering researches [20-27]. In this study, a Multi Layer Perceptron (MLP) neural network is utilized in order to develop an appropriate model for the prediction of products selectivity. MLP This type of network consists of an input layer, an output layer and one or more hidden layers (Figure 2). The number of neurons in the input and the output layer depends on the number of input and output parameters respectively. However, the hidden layer may contain desired neurons. All the layers are interconnected as shown in Figure (2) and the strength of the interconnections is determined by the weights associated with them. Each input of neurons (p) is weighted with an appropriate (w), the sum of the weighted inputs and the bias (b) forms the input to the transfer function (f). Neurons can use any differentiable transfer function f to generate their output (n) and is given as: Multilayer Perceptron networks often use the log-sigmoid transfer function (Eq. 8); however, other functions are commonly used. In this work, one hidden layer was chosen for the networks (as shown in Figure 2), and the optimum neuron numbers for hidden layer was calculated. By using Bayesian regularization back propagation, the MLP neural network was trained. This training method updates the weight and bias values according to the Levenberg- Marquardt optimization [28]. It minimizes a combination of squared errors and weights, and then determines the correct combination so as to produce an artificial neural network that generalizes well. Training was carried out until the mean absolute relative error (MARE) which represented by Eq. 9, was minimized. When the training was terminated, to avoid over learning, the error of test and training data were calculated. As illustrated in Figure (2), the inputs of the proposed networks are DO value, bacteria OD and HS load, and the outputs are elemental sulfur, sulfate and thiosulfate selectivity. To choose the best network structure, different configurations of MLP networks were trained and tested. Network parameters such as: numbers of hidden layers, numbers of neurons in each hidden layer, transfer functions and training algorithm were studied in this attempt. Eventually, the network structure that produced the smallest error for testing and training data was determined. The needed experimental data were measured at different DO values, bacteria ODs and HS loads, and comprise 300 observations. Using the random selection method, 75% of all data (225 data sets) were assigned to the training set, while the rest of the data were used as the validation set. Results and Discussions A part of the obtained experimental data are shown in Table (1). According to the results, increasing of bacteria OD leads to more sulfur and sulfate selectivity, but leads to decrease of thiosulfate selectivity. Although, by increasing DO value, sulfate and thiosulfate selectivity increase, and sulfur selectivity decreases. In addition, increasing sulfide load leads to increase of elemental sulfur and thiosulfate selectivity, and decrease in sulfate selectivity. After many attempts, the best ANN obtained is a MLP with one hidden layer. The optimum number of neurons in the hidden layer is 15 neurons as shown in Figure (3). The transfer function of the first layers is a hyperbolic tangent sigmoid (Eq. 9) and that of second layer is a positive linear function. The parameters of the ANN structure are shown in Table (2). As shown in Figure (4) the ANN model has been able to capture all the features of the system reasonably and can be used for estimating the product selectivity within the range in which it has been trained. Figure (4a) compares the results of applying the training data and Figure (4b) compares the applying test data to the MLP with experimental data at different conditions. The correlation coefficient (R2) value of the ANN model is near to one, which indicates a good accuracy of the ANN model. The relative error between experimental data and calculated values, for verification data are illustrated in Figure (5). As seen in this figure, mean absolute relative error (MARE%) for sulfate, sulfur and thiosulfate selectivity are 4.4, 1.77 and 0.23% respectively. The results show that the proposed model is in a good agreement with experimental data which ANN did not observe in the training phase. Conclusion In biological sulfide removal, elemental sulfur production should be maximized to save more hydroxyl ions. Thus, the prediction of product selectivity is essential in the design of the biological sulfide removal system. An artificial neural network based model was developed for the prediction of product selectivity as a function of DO, OD of bacteria and HS load, in the biological sulfide removal system. The best architecture of the MLP network was obtained by trial. Application of the proposed ANN model for training and test data indicates that it can predict the product selectivity with a considerable accuracy.

Challenges in Motivating Employeess Essay

Why motivating employees is becoming increasingly challenging. The study of motivation is concerned basically, why people behave in a certain way or why people do what they do? Generally motivation can be described as the direction and persistence of action. However different items such as work environment or internal and external forces can influence the person’s choice of action. Managers face a significant challenge in finding ways to motivate their employees. Some employees can often if hard to get motivated for a job even after being employed. Maybe because there is a day job duties repletion, work condition and etc. Below I discuss some of the most significant challenges in employee’s motivation: Money is one of the major variables of satisfaction and motivation, no doubt that employees will feel less motivated if they feel their compensation is not appropriate. Paying employees less will lead to dissatisfaction and of course dissatisfied employee is an unmotivated employee. However we have to consider individual differences in the motivation. Employees have different needs and given reward do not motivate all individuals similarly. Managers should spend time necessary to understand what is important to each employee and the align goal, level of involvement and reward with individual needs. For example professionals and knowledge workers which highly trained with a college or university degree are more concerned with content of work rather than their place on the organization chart. Money and promotions typically are low on their priority list in contrast job challenge and having skill-development opportunities tend to rank high. However motivating low skilled service workers which pay levels are often little above minimum wage such as retailing and fast food is different. Many employees working in low skilled service jobs feel that they do not get the respect they deserve from their employers. Unless pay and benefits are significantly increased, high dissatisfaction is probably inevitable in these jobs. Trying to understand the needs of such employees might help motivate them better. Motivating employees in a unionized workplace environment is another challenge. For example unions have not been very receptive to pay for performance plans. They believe that differential pay to employees doing similar work can hurt corporation and lead to competition in the workplace. In other word in unionized companies providing opportunities for training and advancement and listening to employees concern all help in creating a more positive environment. On the other hand with todays globalized companies we have to consider motivation do not necessarily work equally through the world. Reward practices in different countries are variable based on cultural differences. For example countries that put a high value on uncertainly avoidance prefer pay base on objective such as seniority. Countries that put a high value on individualism place more emphasis on an individual’s responsibility for performance that leads to rewards. Countries that put a high value on human orientation offer social benefits and programs that provide working family balance, such as childcare, maternity leave and etc. Work conditions and environment is another motivation challenges. Changing the way workers are treated may boost productivity more than changing the way they are paid. An employee who feels his working conditions are unreasonable maybe unmotivated. If he feels completing a task would place him in a dangerous situation, he may not see the value doing or completing it. Other thing which could affect employee motivation is training. An employee being asked to do work which he is not qualified for or capable of doing can result in an unmotivated employee. People are generally the most motivated when their jobs give them an opportunity to learn new skills and tasks that are performed and enable them to demonstrate competence. So no doubt having an open, safe and welcoming environment is one of the most important factors. Regulation of the hours of work is another condition. If an employee is working 60 hours a week instead of the standard 40 hours, he may feel unmotivated to show up or give a full effort knowing that he will give more work and expected to stay late to finish project. Multicultural Team is another challenge in motivation. Nowadays most of the companies from big to small have multicultural team. People from diverse culture, background and beliefs. It’s clear we cannot motivate multicultural teams the same way we motivate teams with members all from same culture. Multicultural teams are differing from same culture teams in a variety of aspects. For example people from different culture have different communication style, working method and decision making practices. The expectations of team behavior vary among nationals and ethic cultures. In result managers in these companies have special challenges for motivation. Managing and motivating employees who respect different cultures can be simultaneously exciting and challenging, provide supervisors and managers understand how culture differences inspire organizational excellence, at the same time, employers encounter challenges by separating employees instead of using management and motivation techniques that focus on common traits through the workforce. In these companies as a motivating factor, money is important but only to some extent. Usually After they start getting a fairly level of compensation for their input, money stop being the greatest motivator for most people. Knowing the strength, weaknesses and performance history of each team member are very important. Some training such as multicultural awareness, team building and intercultural management workshop, motivate multicultural team members are very helpful but they must know why they are being trained. Result: All said and done working with a cross cultural team is significantly challenging as the manager must develop strategies to cope not only with differences but also motivate the team to be productive and efficient. Can a manager or supervisor â€Å"empower† an employee? Generally managers used their power as the part of their interaction with employees. In today’s workplace, there is a movement toward sharing more power with employees by putting them in teams and also by making them responsible for some of the decisions regarding their jobs. Some managers believe that to empower people is a real part of leadership as opposed to management and they give examples of way empowerment can actually set people set free to do the jobs they are capable of and also allow them to do self-managing. However managers have different concepts of empowerment, for example one group of executives believed that empowerment was about delegating decisions making within a set of clear boundaries. While another group believed that empowerment was a process of risk taking and personal growth. There is a lot of positive press on empowerment but much of the talk of empowerment, but much of the talk of empowerment, does not result in employees being empowered. Some managers have difficulties letting employees have more power. But most of the managers agree that the employees should understand how their jobs fit into the organization and that they are able to make decisions regarding job action in light of the organization purpose and mission. Empowerment can offer a number of potential benefits throughout all levels of organization. Although there is a continuing debate about the real benefits of empowerment, there appears to be a general assumption motivated staff, quality customer service and improved profits. However all the theories share a common assumption that workers are an untapped resource with knowledge and experience and an interest in becoming involved, and employers need do provide opportunities and structures for their involvement. It’s also assume that participative decision making is likely to lead to job satisfaction and better quality decision and that gains are available to employer (Increased Efficiency) and workers (Job satisfaction), in short an everyone win scenario. According to Erstad, among the many fashionable management refers to the change strategy with the objective of improving both the individuals and the organization’s ability to act. From the context of articles especially in this area empowerment is a complex process. In order to be successful it requires a clear vision, a learning environment, both for management and employees, participation and implementation tools and techniques.

Filibuster Rules in the U.S. Senate

A filibuster is a tactic used in the U.S. Senate to delay votes on controversial legislation or stifle debate on a topic. Typically, a senator wishing to filibuster will ask to speak on the floor of the chamber and, in an attempt to stall legislation action, hold forth for hours at a time. There are few rules that govern a filibuster because the Senate believes its members have the right to speak as long as they want on any issue.   The filibuster dates to the early 1800s. The record  for the longest filibuster is held by  the late U.S. Sen. Strom Thurmond of South Carolina, who spoke for 24 hours and 18 minutes against the Civil Rights Act of 1957, according to U.S. Senate records. In the modern era,  Republican U.S. Sen. Rand Paul of Kentucky staged a daylong filibuster in 2013 that captivated conservatives and libertarians as well as the national news media. Critics call the filibuster unconstitutional at worst and unfair at best. Others believe it to be a  historical relic. Practitioners of the filibuster insist that it protects the rights of the minority against the tyranny of the majority. By their nature, filibusters are meant to draw attention to a specific issues and have the potential to inspire compromise. According to the U.S. Senate, the word filibuster comes from a Dutch word meaning pirate and was first used more than 150 years ago to describe efforts to hold the Senate floor in order to prevent action on a bill. One Way to Break a Filibuster Filibusters rules allow the delay tactic to go on for hours or even days. The only way to force the end of a filibuster is through parliamentary procedure known as  cloture, or Rule 22, which was adopted in 1917.  Once cloture is used, debate is limited to 30 additional hours of debate on the given topic. Sixty members of the 100-member Senate must vote for cloture to stop a filibuster.  At least 16 members of the Senate must sign a cloture motion or petition that states: We, the undersigned Senators, in accordance with the provisions of Rule XXII of the Standing Rules of the Senate, hereby move to bring to a close the debate upon (the matter in question). Important Dates in the History of the Filibuster ​Heres a look at some of the most important moments in the history of the filibuster and cloture. 1806:  The U.S. Senate amends its rulebook in a way that unwittingly allows a member or members to stall action by speaking for hours on end. The Senate, acting at the request of Vice President Aaron Burr, eliminated a provision called the previous question rule that allowed the chamber to cut off floor debate. Without such a measure in place, a senator was permitted to speak indefinitely, paving the way for the filibuster.1841: Henry Clay  threatens to change the Senates  filibuster rules to allow the majority to close debate when Democrats blocked a bank bill.1872: Vice President Schuyler Colfax rules that under the practice of the Senate the presiding officer could not restrain a Senator in remarks which the Senator considers pertinent to the pending issue.1919: First use of Rule 22 when Senate invoked cloture to end debate against the Treaty of Versailles.1935: Populist U.S. Sen. Huey Long of Louisiana filibusters  for 15 hours and 30 minutes trying, without success, to k eep Senate oversight of National Recovery Administrations senior employees. How was he able to speak so long? He recited  Shakespeare and read recipes for pot-likkers, a Southern term for the  broth created by cooking greens.1957: U.S. Sen.  Strom Thurmond  of South Carolina filibusters  for a record 24 hours and 18 minutes as part of a move that successfully blocked the Civil Rights Act of 1957.1964: U.S. Sen.  Robert Byrd of West Virginia filibusters  for 14 hours and 13 minutes in an unsuccessful attempt to block the Civil Rights Act of 1964.1968: The Abe Fortas appointment to succeed Earl Warren as Chief Justice of the Supreme Court is  derailed by Republicans through filibuster.2013:  Republican U.S. Sen. Rand Paul of Kentucky filibusters for nearly 13 hours  to question and raise awareness of the U.S. governments use of drones. It is the  ninth-longest filibuster in history.  I will speak until I can no longer speak, he said.  Paul ended his filibuste r because he had to go to the bathroom. [This articled was updated in May  2018 by Tom Murse.]

Steve Jobs - 595 Words

Leandro Aristy Rodriguez 11/13/2014 Human Resource Management Page 393 #1 , 3 Chapter 12 Assignment 1. A. An accountant at a manufacturing company should be paid by salary plus bonuses depending on the performance of the organization. Accountants are very important in organizations because they are the ones who manage the income of the organization, that’s why accountants should have a set salary and they should receive bonuses as well if the company achieve their goals. B. A salesperson for a software company should be paid Salary or Hourly wages and they should receive commission for their sales or bonuses depending on the organization they work for. For example, if the salespeople for T-Mobile would get paid the same†¦show more content†¦Some of the good things about linking incentive pay to individual performance is that the employee in order for them to receive a good compensation they have to put in work and achieve the organizational goals. Organizations are able to benefit from these type of incentive pay because they are able to meet their goals because the employee tries to do the best they can to meet those goals in order for them to receive more money. Some of the negative things about linking incentive pay to individual performance is that some places tend to get more business than others and that creates a different type of pay between employees who have the same position. For example, a sales person who works in a ATamp;T stor e at the mall and gets paid by commission will receive more commission than a sales person who works at a ATamp;T store at a street store with low traffic because the traffic in the mall is higher which means that the store in the mall will receive more customers. 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