**Chapter 7**

Investigating Bacterial Communities Resistant to Heavy Metal and PAHs Pollutants in the Persian Gulf and Their Ability to Remove These Compounds (a Review)

*Razieh Lamoochi and Kobra Jalali*

## **Abstract**

The Persian Gulf is one of the most diverse water environments in the world. There are various types of marine creatures including corals, sponges and fish in this marine environment. These pollutant sources in the Persian Gulf include oil spills from oil tanks, shipping accidents, marine transportation and oil extraction processes. Since nearly 60% of the world's oil is transported to the Persian Gulf, oil pollution is inevitable. This has made the marine environment the most polluted sea in the world. Bioremediation can be defined as the removal of pollutants such as heavy metals and PAHs from solution by biological agents such as bacteria, fungi, microalgae and yeasts. Various species of bacteria have been isolated and reported by several researchers in the Persian Gulf, perhaps due to its high resistance to a wide range of petroleum hydrocarbons and heavy metals. Therefore, in this chapter, we decided to review the studies conducted in the field of isolating and identifying native bacteria and evaluating their ability to remove heavy metals and polycyclic aromatic hydrocarbons in the Persian Gulf.

**Keywords:** Persian gulf, PAHs, heavy metals, bioremediation, bacteria


**8. Conclusions**

LB continues to play a central role in the evaluation of patients with suspected liver disease, but many aspects of the procedure remain controversial. For example, the precise degree of serum ALT elevations that should prompt a LB is debated, as is the need for LB in all pa‐ tients with suspected NAFLD and chronic hepatitis C. The importance of LB in arriving at a diagnosis of diffuse parenchymal liver disease is being diminished by accurate blood testing strategies for chronic viral hepatitis, autoimmune hepatitis, and primary biliary cirrhosis. Further, imaging tests are superior to LB in the diagnosis of primary sclerosing cholangitis. However, many cases remain in which diagnostic confusion exists even after suitable labo‐ ratory testing and imaging studies. Diagnosing infiltrative disease (eg, amyloidosis, sarcoi‐ dosis), separating benign fatty liver disease from steatohepatitis, and evaluating liver parenchyma after liver transplantation are best accomplished by LB.

[3] Sorbi D, McGill DB, Thistle JL, et al. An assessment of the role of liver biopsies in asymptomatic patients with chronic liver test abnormalities. Am J Gastroenterol

Liver Biopsy: An Overview http://dx.doi.org/10.5772/52616 17

[4] Skelly MM, James PD, Ryder SD. Findings on liver biopsy to investigate abnormal liver function tests in the absence of diagnostic serology. J Hepatol 2001;35:195-199.

[5] Powell EE, Jonsson JR, Clouston AD. Steatosis: co-factor in other liver diseases. Hep‐

[6] Zein CO, Angulo P, Lindor KD. When is liver biopsy needed in the diagnosis of pri‐

[7] Dahlin DC, Stauffer MH, Mann FD. Laboratory and biopsy diagnosis of amyloidosis.

[8] Polson J, LeeWM. AASLD position paper: the management of acute liver failure.

[9] Takamori R, Wong LL, Dang C, et al. Needle-tract implantation from hepatocellular cancer: is needle biopsy of the liver always necessary? Liver Transplant 2000;6:67–72.

[10] European Association for the Study of the Liver. EASL Clinical Practice Guidelines:

[11] Gleeson D, Heneghan MA; British Society of Gastroenterology. British Society of Gastroenterology (BSG) guidelines for management of autoimmune hepatitis. Gut

[12] Verma S, Gunuwan B, Mendler M, et al. Factors predicting relapse and poor outcome in type I autoimmune hepatitis: role of cirrhosis development, patterns of transami‐ nases during remission and plasma cell activity in the LB. Am J Gastroenterol

[13] European Association for the Study ofThe Liver. EASL Clinical Practice Guidelines: Management of chronic hepatitis B virus infection. J Hepatol 2012;57(1):167-85.

[14] European Association for the Study of the Liver. EASL Clinical Practice Guidelines

[15] European Association for the Study of the Liver. EASL Clinical Practice Guidelines:

[16] European Association for Study of Liver. EASL Clinical Practice Guidelines: Wilson's

[17] Pratt DS, Kaplan MM. Evaluation of abnormal liver-enzyme results in asymptomatic

[18] Bianchi L: Liver biopsy in elevated liver function tests? An old question revisited. J

management of cholestatic liver diseases. J Hepatol 2009;51(2):237-67.

for HFE hemochromatosis. J Hepatol 2010;53(1):3-22.

disease. J Hepatol 2012;56(3):671-85.

Hepatol2001;35:290-294.

patients. N Engl J Med 2000;342:1266-71.

Management of hepatitis C virus infection. J Hepatol. 2011;55(2):245-64.

mary biliary cirrhosis? ClinGastroenterolHepatol 2003;1:89-95.

2000;95:3206-3210.

atology 2005;42:5-13.

2011;60(12):1611-29.

2004;99:1510-1516.

Med Clin North Am 1950;34:1171-1176.

Hepatology 2005;41:1179-1197.

Percutaneous LB is contraindicated in patients with severe coagulopathy and ascites, but the degree of coagulopathy that contraindicates a LB is controversial. Also controversial are the technical aspects of LB, particularly the choice of needle (cutting vs. suction) and the use of US to mark or guide the biopsy site. Bleeding is the major complication of LB, with a risk of 0.3%; cutting needles are more likely to cause hemorrhage than are suction needles. While needle biopsy is still the mainstay in diagnosing hepatic fibrosis, its days of dominance seem limited as technology improves. When physical examination or standard laboratory tests reveal clear-cut signs of portal hypertension, LB will seldom add useful information. Similarly, when imaging studies provide compelling evidence of cirrhosis and portal hypertension, needle biopsy is not warranted. The combination algo‐ rithms warrant further evaluation in all chronic liver diseases, as they may help decrease the number of liver biopsies required. Moreover, transient elastography is playing an ev‐ er-increasing role in the assessment of hepatic fibrosis and will significantly reduce the need for biopsy in patients with liver disease.

Clearly, as our knowledge of various liver disorders advances and new especially non-inva‐ sive diagnostic tests are developed, the role of LB in medical practice will continue to evolve. Emergence of better imaging techniques, surrogate serological markers of liver fibro‐ sis are among the many new and exciting developments that hold promise for the future.


**Abstract**

Third-sector organisations are a collective term for voluntary and community services, charities, and social enterprises. Within the UK and internationally, a subset provides a crucial and ever-expanding role in mental health care provision, delivering valuable client and community-led services. However, in the UK these organisations are under increased pressure to demonstrate their value, and many are constrained by scarce resources and lack of expertise. The Service Improvement Learning Collaborative was conceived as an innovative model for shared learning to enhance the value of this sector in mental health care support, generating a valuable resource of practice-based learning and promoting the implementation of effective practices. The initiative combines a collaborative learning model with mentorship support and in-depth, data analytical profiling. This collaborative involved a network of six organisations focused on exploring the maximisation of data quality, the minimisation of client attrition, and the optimisation of clinical outcomes. Evaluating the collated data helped identify the many unique challenges facing the sector and evidenced the model as a pragmatic solution for service quality improvement. This chapter provides an overview of the project's methodology, including its underlying rationale, first year of operation, and value of experiential learning for the field.

**Keywords:** service quality development, collaborative learning networks, practice-based evidence, experiential learning, third-sector organisations, mental health


**6. Recommendations**

The authors believe that improving China's light pollution laws and regulations requires improvements to the legislative model, such as special legislation on light pollution. Depending on the actual situation of each city, light pollution impact assessment should be performed according to clear criteria. For example, Seoul proposes that the mayor should strive to maintain the Korean industrial standard street lighting as the standard for light pollution environmental impact assessment and can seek expert advice accordingly. In addition, special department supervision is needed. For example, Seoul clearly stipulates that the mayor should formulate and implement policies to prevent light pollution and establish a light pollution committee. There should be clear accountability and incentive mechanisms, such as in Seoul, and the mayor may choose to reward those who contribute to the prevention of light pollution [16].

Furthermore, in the actual control, data analysis is needed, and the night sky brightness of different cities is collected to conduct a comparative analysis, and the results are obtained and then controlled point-to-point. For example, Wei Jiang et al. [52] used the Luojia 1–01 satellite to analyze and compare the night sky brightness with the brightness of different degrees in Wuhan, Seoul, Haifa and Mexico.

On the other hand, in future research of laws and regulations, the brightness of the night sky should be compared before and after the data in a range of years, so that the practicability of the current rules can be confirmed—for example, Wei Jiang et al. [53] used the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) for data collection from 1992 to 2012 in assessing light pollution in China based on nighttime light imagery and analyzed the degree of light pollution in various regions of China. It was concluded that China's light pollution has significantly expanded in provincial capital cities in the past 21 years. Therefore, by analyzing this, we can see that the degree of light pollution is on the rise when no relevant light pollution regulations are proposed. In the future, it is necessary to continue collecting proper data to compare and analyze the practicability of current rules on light pollution.

At the same time, in future research, the ecosystem needs to be paid attention to in light pollution. We can also conduct data comparison analysis. For example, Jonathan et al. [54] used DMSP/OLS in combination with GLC2000 to collect nighttime lights from 1992 to 2012 and evaluated the latest changes in nighttime artificial illumination for 43 global ecosystem types. The results indicated that the Mediterranean climate–ecosystem has experienced an enormous increase in exposure and that all terrestrial ecosystem types have experienced a certain degree of artificial light exposure and that this exposure is increasing. Through the analysis of the ecosystem, the relevant laws and regulations can be strengthened in the future.

In the era of big data, we not only need to use data collection but also need to listen to the opinions of the public—for example, the public's views on light pollution and its impact on the environment. Lyytimaki et al. [55] investigated the public's views on light pollution as an environmental problem using a questionnaire survey. Of the 2053 responses to the study, 84.6% said light pollution had spread to all areas. A total of 82.9 per cent of the respondents said it was essential to be dark. Various light pollution sources were found in the problem of light pollution sources. The most commonly mentioned sources were street lamps, courtyard lamps, commercial lights, and outdoor decorative lights. Therefore, the author believes that only by combining a questionnaire survey, interviews, and DMSP/OLS data collection, etc. can the occurrence of light pollution be effectively controlled, and the effectiveness of laws and regulations be improved in the future.


**3. Stochastic Simulations**

After the reference building models were validated, stochastic simulations were conducted to consider di fferent building design variations for the urban building energy consumption database development. Eight di fferent design parameters, such as the building envelope's thermal properties, infiltration, heating and cooling schedules, lighting power density, and equipment power density, were considered to cover di fferent constructions, building design scenarios and the occupant's energy usage patterns.

To di fferentiate building vintage, three levels (high, medium, and low) of building envelopes were studied by grouping U factors of di fferent parts (external wall, slab, roof, and glass). The building geometry was kept constant to represent the most common configuration in Wuhan.

To reflect the actual energy saving behavior of the occupants and better capture di fferent energy usage patterns, thirteen heating and cooling schedules were proposed. The schedule information was derived based on statistical analysis of the actual building energy data from CRECS. The data from the CRECS energy consumption survey was ranked from low to high. Level of 5% means the top 5% from the ranking. It represents the most e fficient energy usage, in terms of heating and cooling hours per day and days per year. Level of 95% represents the least e fficient energy usage (bottom 5% from the ranking). It is assumed that the lighting and plug/equipment loads are coupled with heating/cooling schedules, since people's energy saving behavior is consistent. Other energy usage profiles can be interpolated.

Figure 8 shows the stochastic cases of the residential building. In total, 117 design scenarios were considered. The combination of residential building baseline model is highlighted in yellow. In a similar way, 117 small o ffice and 117 large o ffice EnergyPlus models were generated to cover di fferent energy use intensity scenarios for o ffice buildings.

**Figure 8.** 117 stochastic scenarios of the residential building model.


**Acknowledgements**

I would like to thank the funding agency GIZ for a grant to successfully carry out the research in the two states. I would also like to thank the various officials and industry representatives in the two states for their active participation and feedback in preparing the capacity needs assessment. I would especially like to thank Dr. Dieter Brulez, Dr. Hrishikesh Mahadev, and Dr. Rajani Ganta for their useful comments.

*Adapting to Climatic Extremes through Climate Resilient Industrial Landscapes: Building… DOI: http://dx.doi.org/10.5772/intechopen.98732*


**2. Elements of genetic and differential evolution algorithms**

Genetic algorithms and differential evolution algorithms are evolutionary algorithms that typically define objective functions so that the set of parameters being optimized are represented in a vector [3]. Parameter constraints are implemented by restricting the available solution spaces for each parameter in the vector. The basic design strategy for such genetic and differential evolution algorithms is to determine evolutionary operators that balance the algorithm's ability to both effectively search the solution space and converge to an optimal solution.

Genetic algorithms and differential evolution algorithms typically begin by randomly selecting *K*ð Þ 0 vectors, called candidate solutions or individuals, in the solution space. This initial set of candidate solutions is called the initial population. A subset of *M*ð Þ1 of these initial candidate solutions, or individuals, are selected as a function of their fitness, or cost, when evaluated with respect to the optimization criterion. Although many selection operators exist [12, 13], selection operators are typically designed to select a subset of *M*ð Þ1 candidate solutions from the population in a manner that should improve the overall fitness of the population. The candidate solutions that survive the selection operator form the initial mating pool of *M*ð Þ1

individuals where 1≤ *M*ð Þ1 ≤*K*ð Þ 0 . Candidate solutions from the mating pool are then selected for recombination and mutation. Recombination operators create new candidate solutions by combining vector elements from two or more of the candidate solutions from the mating pool. Mutation operators in genetic algorithms create new candidate solutions by altering elements of candidate solution vectors. In differential evolution, mutation operators create new candidate solutions by using vector operations, such as addition and scaling, on two or more of the candidate solutions from the mating pool. Regardless of the algorithm, the new candidate solutions created by recombination and mutation are added to the mating pool to create a new population of *K*ð Þ1 candidate solutions. This process of selection, recombination and mutation forms one generation, or iteration, of a genetic or differential evolution algorithm. This process iterates until a convergence criteria is met and an optimal solution is determined. A generic genetic algorithm or differential evolution algorithm can be summarized as follows:

Initialize population {*K*ð Þ 0 candidate solutions)}

*n* 0

$$\mathbf{repeat}$$

*n n* þ 1

Select *M n*ð Þ candidate solutions using a selection operator Generate new candidate solutions using a mutation operator Generate new candidate solutions using a recombination operator Add the new *K n*ð Þ� *M n*ð Þ candidate solutions to the mating pool **until** convergence condition is met

where *n* is the algorithm's iteration number.

Genetic operators such as recombination and mutation generate a combination of new candidate solutions that can be either similar or diverse from the candidate solutions in the mating pool. Controlling the ratio of the diversity and similarity of new candidate solutions added to a population each generation is a fundamental design parameter of any search algorithm [14]. Creating diversity, or exploration, is the process of generating candidate solutions that lie in previously unevaluated regions of the search space. Creating similarity, or exploitation, on the other hand, is the process of generating candidate solutions within a neighborhood of previously visited points so as to converge to an optimal point in the neighborhood. Exploration and exploitation are typically conflicting processes of a search algorithm because a lack of diversity can result in a population converging to a local minima or maxima and a lack of similarity can impede convergence. Therefore, every search algorithm needs to design an effective ratio of exploration and exploitation of a search space. In general, an optimal ratio of diversity and similarity is not only dependent on the search algorithm but also the cost, or objective, function. For example, determining the optimal solution of a unimodal objective function typically requires less exploration than determining an optimal solution of a multi-modal objective function that typically requires more exploration. Also, different generations, or iterations, of a search algorithm typically have a different optimal ratios of exploration and exploitation. For example, a search algorithm's early generations require more exploration than exploitation until the neighborhood of the optimal solution is found. After the neighborhood of the optimal solution is found, a search algorithm's generations require more exploitation and less exploration. Therefore, the goal of any search algorithm is to design a ratio of adding diverse new candidate solutions and similar new candidate solutions to each generation so that the algorithm can effectively determine optimal solutions for different types of objective functions.

Genetic algorithms and differential evolution algorithms typically use three operators, selection, mutation and recombination, for controlling the ratio of adding diverse and similar new candidate solutions to their populations. Selection operators control the ratio of exploration and exploitation by varying the selection process. A selection operator that is designed to select the most fit candidate solutions, the candidate solutions with the best costs when evaluated with respect to the optimization criterion, biases the selection process away from exploration and towards exploitation. A selection operator that is designed to select the least fit candidate solutions biases the selection process away from exploitation and towards exploration.

Mutation operators for a typical genetic algorithm or differential evolution algorithm randomly modify individuals from the mating pool to increase the diversity of a population. As a result, a typical mutation operator increases the exploration of unevaluated regions of the search space. However, some mutation operators only slightly alter individuals from the mating pool. In such cases, these types of mutation operators can be classified as an exploitation operator because most of the mutated individual is preserved and the mutated individual still remains in the neighborhood of the parent candidate solution. A recombination operator for a typical genetic algorithm or differential evolution algorithm combines two or more parent individuals, or candidate solutions, from the mating pool to generate a new and possibly more fit candidate solution. As a result, a typical recombination operator generates new candidate solutions within a neighborhood of the parent candidate solutions. From this perspective, a recombination operator increases exploitation and improves the convergence of the algorithm. However, when recombination uses a mutated candidate solution, recombination can create a new candidate solution in a previously unevaluated region of the search space. In such cases, recombination operators can improve exploration of the search space. In most cases, mutation operators improve a population's diversity and recombination operators improve an algorithm's convergence rate; however, in practice, the combination of selection, mutation and recombination determines the ratio of exploration and exploitation in both genetic algorithms and differential evolution algorithms.


**Author details**

Igor V. Bezmenov "VNIIFTRI", Mendeleevo, Moscow region, Russian Federation

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Effective Algorithms for Detection Outliers and Cycle Slip Repair in GNSS Data Measurements DOI: http://dx.doi.org/10.5772/intechopen.92658*


**4. Conclusions**

The important conclusions drawn from this study are as follows:

*Study on the Perspective of Mechanical Properties and Corrosion Behaviour of Stainless… DOI: http://dx.doi.org/10.5772/intechopen.101388*


**3. The design of programmable stamping operation**

The most outstanding advantage of the servo press is the flexibility, i.e. the programmability of its ram motions to accommodate different stamping operations such as drawing, stamping, blanking, coining, etc. To design the stamping operation, a pseudo-NURBS method is presented to model the ram motion where the displacement and velocity demands of the stamping operation (ram motion) are transformed to weight constrains, and inequalities are formed by using weights as variables. An optimized ram motion can be generated to match the deformation of work piece material that avoids large transient force and vibration by smooth approaching and slow releasing of the ram, and improves stamping productivity via quick return of the ram.

### **3.1. The pseudo-NURBS representation of stamping operation**

The ram of the press only moves along a line segment within a stroke reciprocatedly and the ram motion is changeable in terms of time. To design the ram motion, a reasonable mathematical expression should be determined firstly. As a well-known tool in computer graphics and CAGD [18], the NURBS offers a common mathematical representation for freeform surfaces/curves and commonly used analytical shapes such as natural quadrics, torii, extruded surfaces and surfaces of revolution. It is easy and straightforward to change the shape through the manipulation of control points, weights and knots. And degree elevation, splitting, knot insertion and deletion and knot refinement offer a wide range of tools to design and analyze shape information. In this chapter, a pseudo-NURBS method is proposed to express the ram motion by introducing time parameter into the NURBS representation. A NURBS curve is a defined by the following equation:

$$\text{acc}(u) = \Sigma\_{i=0}^{\text{pt}} \mathbf{P}\_i \mathbf{R}\_{i,k}(u), \qquad \mathbf{R}\_{i,k}(u) = \frac{a \mathbf{e}\_i^{\text{N}} \mathbf{i}\_{i,k}(u)}{\sum\_{i=0}^{\text{pt}} a \mathbf{e}\_i^{\text{N}} \mathbf{i}\_{i,k}(u)} \tag{16}$$

The Design of a Programmable Metal Forming Press and Its Ram Motion 167

**Pi** +**5**

= − (19)

− = =+ = (20)

= = …− (21)

= (23)

j j,k i i,k j j,k i i,k i 0 i 0 2ω N (x) ω N (x) ω N (x) ω N (x) = = − +

**Pi**+**2 Pi**+**3**

**Qi** +**2 Qi** +**3**

3. boundary constraints: The velocity and acceleration at the firstpoint and the endpoint

r r1

r r1

−

*j np*

(i) (i) (j) (j) C (t ) D , C (t ) D ,..... 1,2, , , 1,2, , 0s ne = = =… =… *i sp j ep* (22)

i,k i

= − (24)

n

r 1 d |Q Q | − =

−

jp1 j p i i j

=

+ −

1

p

+

the control points of the same number need to be determined.

i 0

=

0 r r1 n |Q Q | t 0, t t ,t 1 d

where, *Qr* is curve point, *n* is the number of the curve points, and *tr* is intermittent element of

u t 1, 2, ,

Hence, a pseudo-NURBS expression is proposed for the ram motion modeling. To assure the velocities and accelerations of the ram at the first points and endpoints zero, following

where, (i) C (t ) 0 refers to *i*-th derivative at the first point, (j) C (t ) n means *j*-th derivative at the endpoint. Equations with the sum number of *sp*+*ep*+1 are formed from Eqs.(16) and (22) and

n (j) (j)

i 0 C (x) R (x)P =

2 n n n (1) (1) (1) j,k j j,k i i,k i j,k i i,k i i,k

R (x) ω N (x) / ω N (x) ω N (x) ω N (x) / ω N (x)

i 0 i 0 i 0

= = =

= n n (1) (1) (2)

**Qi** +**4**

**Qi** +**5**

**Pi**

should be equal to zero for the stamping cycle. 4. knot insertion. The chord method is applied as follows:

**Figure 11.** Control points and curve points

knot vector. The knot is expressed as

constraints should be followed:

n (2) (2) j,k j j,k i i,k

R (x) ω N (x) / ω N (x)

**Pi**+**1**

**Qi**

**Qi** +**1**

where, P*i* are vectors composed of *x* and *y* coordinates of the control points, *i* ω are positive real weights for each control point, and point of *c*(*u*) is called curve point. ( ) , *R u i k* are NURBS basis functions, and ( ) , *N u i k* are B-spline basis functions of degree *k*-1. The B-spline basis function ( ) , *N u i k* is expressed by de Boor-Cox [8] as follows:

$$N\_{\vec{i}\_{\prime},0}(u) = \begin{cases} 1, u\_{\vec{i}} \le u \le u\_{\vec{i}+1} \\ 0,
otherwise \end{cases} \tag{17}$$

$$N\_{i,k}(u) = \frac{u - u\_i}{u\_{i+k} - u\_i} N\_{i,k-1}(u) + \frac{u\_{i+1} - u}{u\_{i+k+1} - u\_{i+1}} N\_{i+1,k-1}(u) \tag{18}$$

where, the interval [*u*i, *u*i+1] is called the *i*th knot span, and *U*={*u*0, *u*1, …*um*} (*m=n+k+*1) is the knot vector. A knot vector is non-periodic if the first and last knots are repeated with multiplicity *k*+1, i.e. *U*={*u*0, *…*, *u*0, *uk*+1, …., *um*-*k*-1, *u*m,…*um*}. For most practical applications, *u*0 = 0 and *um* = 1. Knots *uk*+1, …, *um*-*k*-1 are called interior. For planar curve, *U* = {0, 0, 0, *uk*+1, … *um-k-*1, 1, 1, 1}.

The most significant properties of the NURBS curve are:

Different from the geometric shape, the time parameter *t* should be introduced in order to express the ram motion. To apply NURBS expression for the ram motion, following issues are considered:

**Figure 11.** Control points and curve points

graphics and CAGD [18], the NURBS offers a common mathematical representation for freeform surfaces/curves and commonly used analytical shapes such as natural quadrics, torii, extruded surfaces and surfaces of revolution. It is easy and straightforward to change the shape through the manipulation of control points, weights and knots. And degree elevation, splitting, knot insertion and deletion and knot refinement offer a wide range of tools to design and analyze shape information. In this chapter, a pseudo-NURBS method is proposed to express the ram motion by introducing time parameter into the NURBS

> ( ) , ( ) P ( ), ( ) 0 , , ( ) 0 , *N u n i ik cu R u R u i i ik i k n N u i i ik*

real weights for each control point, and point of *c*(*u*) is called curve point. ( ) , *R u i k* are NURBS basis functions, and ( ) , *N u i k* are B-spline basis functions of degree *k*-1. The B-spline basis

> 1, 1 ( ) ,0 0, *u uu N u i i i otherwise* ≤ ≤ + =

1 ( ) ( ) ( ) , , 1 1, 1 1 1 *u u u u i i Nu N u N u i k uu u u i k i k ik i ik i* − − + = + − − − + − + ++ +

where, the interval [*u*i, *u*i+1] is called the *i*th knot span, and *U*={*u*0, *u*1, …*um*} (*m=n+k+*1) is the knot vector. A knot vector is non-periodic if the first and last knots are repeated with multiplicity *k*+1, i.e. *U*={*u*0, *…*, *u*0, *uk*+1, …., *um*-*k*-1, *u*m,…*um*}. For most practical applications, *u*0 = 0 and *um* = 1.

• Locality: A movement of the control point Pi affects the curve only in the interval [ui,

• Affine and projective invariance: A NURBS curve is affinely and projectively invariant, i.e. transforming the curve by an affine or perspective transform is equivalent to

Different from the geometric shape, the time parameter *t* should be introduced in order to express the ram motion. To apply NURBS expression for the ram motion, following issues

1. time parameter *t*: To build the relationship of the ram displacement with respect to time, the stamping time and displacement are normalized by dividing by stampling

2. control points and curve points: As shown in Figure 11, the control points *Pi* should be

Knots *uk*+1, …, *um*-*k*-1 are called interior. For planar curve, *U* = {0, 0, 0, *uk*+1, … *um-k-*1, 1, 1, 1}.

• Differentiability: Same differentiability properties as the basis functions.

determined according to Eq. (16) given the curve point *Qi*.

= = = =

where, P*i* are vectors composed of *x* and *y* coordinates of the control points, *i*

ω

ω

(16)

ω

(17)

are positive

(18)

representation. A NURBS curve is a defined by the following equation:

function ( ) , *N u i k* is expressed by de Boor-Cox [8] as follows:

The most significant properties of the NURBS curve are:

transforming the control points.

cycle and stroke respectively.

ui+k+1].

are considered:

$$\mathbf{cd} = \sum\_{\mathbf{r}=1}^{\mathbf{n}} |\mathbf{Q}\_{\mathbf{r}} - \mathbf{Q}\_{\mathbf{r}-1}| \tag{19}$$

$$\mathbf{t}\_0 = \mathbf{0}, \qquad \mathbf{t}\_r = \mathbf{t}\_{r-1} + \frac{|\mathbf{Q}\_r - \mathbf{Q}\_{r-1}|}{\mathbf{d}}, \quad \mathbf{t}\_n = 1 \tag{20}$$

where, *Qr* is curve point, *n* is the number of the curve points, and *tr* is intermittent element of knot vector. The knot is expressed as

$$\mathbf{u}\_{j+p} = \frac{1}{\mathbf{P}} \sum\_{i=j}^{j+p-1} \mathbf{t}\_i \qquad j = 1, \ 2, \ldots, n-p \tag{21}$$

Hence, a pseudo-NURBS expression is proposed for the ram motion modeling. To assure the velocities and accelerations of the ram at the first points and endpoints zero, following constraints should be followed:

$$\mathbf{C}^{(\mathbf{i})}(\mathbf{t}\_{0}) = \mathbf{D}\_{\mathbf{s}}^{(\mathbf{i})}, \; \mathbf{C}^{(\mathbf{0})}(\mathbf{t}\_{\mathbf{n}}) = \mathbf{D}\_{\mathbf{e}}^{(\mathbf{0})}, \dots \\ \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{sp}, \mathbf{j} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{ep} \tag{22}$$

where, (i) C (t ) 0 refers to *i*-th derivative at the first point, (j) C (t ) n means *j*-th derivative at the endpoint. Equations with the sum number of *sp*+*ep*+1 are formed from Eqs.(16) and (22) and the control points of the same number need to be determined.

$$\mathbf{C}^{(0)}(\mathbf{x}) = \sum\_{i=0}^{n} \mathbf{R}\_{i,k}^{(0)}(\mathbf{x}) \mathbf{P}\_i \tag{23}$$

$$\mathbf{R}\_{\mathbf{j},\mathbf{k}}^{(\mathrm{I})}(\mathbf{x}) = \omega\_{\mathbf{j}} \mathbf{N}\_{\mathbf{j},\mathbf{k}}^{(\mathrm{I})}(\mathbf{x}) / \sum\_{\mathbf{i}=\mathbf{0}}^{\mathrm{n}} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}(\mathbf{x}) - \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{j},\mathbf{k}}(\mathbf{x}) \sum\_{\mathbf{i}=\mathbf{0}}^{\mathrm{n}} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}^{(\mathrm{I})}(\mathbf{x}) / \left[ \sum\_{\mathbf{i}=\mathbf{0}}^{\mathrm{n}} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}(\mathbf{x}) \right]^{2} \tag{24}$$

$$\mathbf{R}\_{\mathbf{j},\mathbf{k}}^{(2)}(\mathbf{x}) = \omega\_{\mathbf{j}} \mathbf{N}\_{\mathbf{j},\mathbf{k}}^{(2)}(\mathbf{x}) / \sum\_{\mathbf{i}=0}^{n} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}(\mathbf{x}) - \left[ 2\omega\_{\mathbf{j}} \mathbf{N}\_{\mathbf{j},\mathbf{k}}^{(1)}(\mathbf{x}) \sum\_{\mathbf{i}=0}^{n} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}^{(1)}(\mathbf{x}) + \omega\_{\mathbf{j}} \mathbf{N}\_{\mathbf{j},\mathbf{k}}(\mathbf{x}) \sum\_{\mathbf{i}=0}^{n} \omega\_{\mathbf{i}} \mathbf{N}\_{\mathbf{i},\mathbf{k}}^{(2)}(\mathbf{x}) \right]$$

$$\left| \left[ \sum\_{i=0}^{n} \omega\_{i} \mathbf{N}\_{i,\mathbf{k}} \langle \mathbf{x} \rangle \right]^{2} + 2 \,\omega\_{\rangle} \mathbf{N}\_{\mathbf{j},\mathbf{k}} \langle \mathbf{x} \rangle \left[ \sum\_{i=0}^{n} \omega\_{i} \mathbf{N}\_{i,\mathbf{k}}^{(1)} \langle \mathbf{x} \rangle \right]^{2} / \left[ \sum\_{i=0}^{n} \omega\_{i} \mathbf{N}\_{i,\mathbf{k}} \langle \mathbf{x} \rangle \right]^{3} \tag{25}$$

$$\mathbf{N}\_{i,\mathbf{p}}^{(\mathbf{j})}(\mathbf{x}) = \mathbf{p}(\frac{\mathbf{N}\_{i,\mathbf{p}-1}^{(\mathbf{j}-1)}(\mathbf{x})}{\mathbf{u}\_{i+\mathbf{p}} - \mathbf{u}\_{i}} - \frac{\mathbf{N}\_{i+1,\mathbf{p}-1}^{(\mathbf{j}-1)}(\mathbf{x})}{\mathbf{u}\_{i+\mathbf{p}-1} - \mathbf{u}\_{i+1}}) \tag{26}$$

The Design of a Programmable Metal Forming Press and Its Ram Motion 169

0.6, 0.8, 0.05, 0.8, 1.2, 0.5, 0.8, 1], and *U*2 = [0, 0, 0, 0, 0.0476, 0.1111, 0.254, 0.4444, 0.619, 0.7619, 0.8571, 0.92, 0.995, 1, 1, 1, 1]. The corresponding ram motions are dicpited in Figure 13 resepctively. It can be found that the peak velocity and acceleration of the return period can be lowered up to 20%-30% and the ram velocity lowered 40% when the upper die touches

the workpiece by adjusting the knots corresponding to quick return period.

(a) displacement (b) velocity (c) acceleration

**Figure 12.** Ram motion under different weights

select by optimization.

*l* , …, and *l*

ω

( ) 0 , ( ) 0 , *n dN u i i i ik n N u i i ik* ω

1 *l* , 2

1*s* ≤

 = =

**Figure 13.** Ram motion under different weights and knot vectors

*3.4.1. The self-adaption due to the ram displacement*

**3.4. The self-adaption of the weights due to optimization of the ram motioin**

(a) displacement (b) velocity (c) acceleration

Under the same group of demands over the displacement, velocity of the ram motion, a large number of NURBS curves can be achieved and hence the optimized one is able to

Due to the local support of the NURBS expression, the ram motion can be discretized into several segments to set up the constraint inequalities. The *m* curve segments are denoted as

*m* . Supposed displacement demands over curve segement *l*

≤ 2*s* , where knot vector *u*∈[ 1 *uj* , 2 *uj* ]. To satisfy this in-equality demand, let

*j* , 1*s* ≤ *l*

*j* ≤ 2*s* , i.e.

Combining Eqs.(16), and (22) through (26), following equation can be got:

(1) (1) 0 0 1 (2) (2) (2) 1 012 2 1234 3 2345 4 3456 5 5678 6 7 5678 8 7 8 9 10 9 8 9 10 11 (2) (2) 10 10 11 11 (1) 11 12 1 P R R P RRR P RRRR P RRRR P RRRR P RRRR P RRRR P P RRRR P RRRR P R R P R P 1 **(2) 12 (1) 12 R R** 0 (1) s (2) s 1 2 3 4 5 6 7 (2) e (1) e 8 Q D D Q Q Q Q Q Q Q D D Q = (27)

By solving Eq.(27), the control points *P*0, *P*1*…*, and *P*12 are determined and therefore the ram motion can be solved according to Eq.(16). Thanks to the locality of NURBS, the ram motion can be refined piecewisely through the manipulation of corresponding control points, weights and knots.

#### **3.2. The influence of weights to the fluctuations of ram velocity and acceleration**

To check the influence of weights to the ram motions, let [*ω*1] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [*ω*2] = [1, 0.5, 0.6, 0.8, 1,0.9, 0.8, 0.4, 0.8, 1.2, 0.5, 0.8, 1], and [*ω*3] = [1, 1, 1, 1, 0.5, 0.6, 0.8, 0.1, 0.8, 1, 1, 1, 1]. The corresponding ram motions are dicpited in Figure 12 resepctively. It can be found that weight *ωi* (*i*=8) should be adjusted that can lower the fluctuation of the ram velocity and acceleration and decrease the shock, vibration and noise during stamping operation, and decrease the peak power demand of the servomotors.


*1.3. Contributions and Novelty of This Study*

A JCHP is more likely to obtain higher COP than a traditional HP under certain circumstances. However, there have been so far just a few studies on the use of JCHP and industrial processes. There is also a gap in the literature for a comparative analysis of different HP types, including the emerging JCHP. The COP equations of HP have some ideal assumptions. These results are in some deviation from the actual performance of the HP, which cannot well represent the real performance of the HP.

This study simulates and optimises the operation of the main classes of HPs in a process simulation software—Petro-SIM [27]—to get the performance of the HP, as much close to the reality as possible. These classes include the VCHP, TCHP and the JCHP.

The setting of various parameters considers possible process configurations. All COP curves and heat duties in the GCC for process and HP integration will be plotted against the actual data calculated in Petro-SIM. The choice of HP type should be performed based on the temperature–enthalpy profile of the considered industrial process for obtaining optimal performance. In this paper, the performance and application scope of three different HP systems—JCHP, VCHP, and TCHP—are discussed and compared to understand the energy-saving potential of applying the HP.

Section 2 introduces the simulation and optimisation of the considered HP types and the method of integration with the process for achieving heat recover. In Section 3, the suitability of the HP types to different temperature–enthalpy (T–H) profiles is evaluated, aiming at the minimisation of power consumption. The optimal COP of each integration case was obtained by optimising the operating parameters of the HP when given source and sink at different temperatures. In Section 4, the model is further applied to the integration of HP and different industrial process cases using Pinch Analysis, and the energy-saving potential of different types of HP is evaluated using the GCC [28].